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arXiv 2025年

作者： Xu, Jianye Alrifaee, Bassam Betz, Johannes Mokhtarian, Armin Mittal, Archak Cai, Mengchi Mangharam, Rahul Shehata, Omar M. Elias, Catherine M. Zaech, Jan-Nico Scheffe, Patrick Jahncke, Felix Ulhas, Sangeet Sankaramangalam Arfvidsson, Kaj Munhoz Department of Computer Science RWTH Aachen University Germany Department of Aerospace Engineering University of the Bundeswehr Munich Germany Professorship of Autonomous Vehicle Systems Technical University of Munich Germany Department of Civil Engineering Indian Institute of Technology Bombay India School of Vehicle and Mobility Tsinghua University China Department of Electrical and Systems Engineering University of Pennsylvania United States Mechatronics Engineering Department Faculty of Engineering and Materials Science The German University in Cairo Egypt Department of Computer Science Engineering The German University in Cairo Egypt INSAIT Sofia University "St. Kliment Ohridski" Bulgaria School for Engineering of Matter Transport and Energy Arizona State University United States Division of Decision and Control Systems KTH Royal Institute of Technology Sweden

This article proposes a roadmap to address the current challenges in small-scale testbeds for Connected and Automated Vehicles (CAVs) and robot swarms. The roadmap is a joint effort of participants in the workshop "1st Workshop on Small-Scale Testbeds for Connected and Automated Vehicles and Robot Swarms," held on June 2 at the IEEE Intelligent Vehicles Symposium (IV) 2024 in Jeju, South Korea. The roadmap contains three parts: 1) enhancing accessibility and diversity, especially for underrepresented communities, 2) sharing best practices for the development and maintenance of testbeds, and 3) connecting testbeds through an abstraction layer to support collaboration. The workshop features eight invited speakers, four contributed papers [1]–[4], and a presentation of a survey paper on testbeds [5]. The survey paper provides an online comparative table of more than 25 testbeds, available at https://***/testbeds-survey. The workshop’s own website is available at https://***/iv24-workshop. Copyright © 2025, The Authors. All rights reserved.

关键词： Testbeds

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AR/VR Digital Twin for Simulation and Data Collection of Robotic Environments

AR/VR Digital Twin for Simulation and Data Collection of Rob...

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IEEE International Conference on Autonomous Robot systems and Competitions (ICARSC)

作者： João G. Martins Karle Nutonen Paulo Costa Vladimir Kuts Tauno Otto Armando Sousa Marcelo R. Petry CRIIS - Centre for Robotics in Industry and Intelligent Systems INESC TEC - Institute for Systems and Computer Engineering Technology and Science Porto Portugal Faculty of Engineering University of Porto (FEUP) Porto Portugal ViruTech - Virumaa innovation center of digitalization and green technologies Taltech Virumaa College Kohtla-Jarve Estonia Department of Mechanical and Indusrial Taltech Tallinn Estonia

ISBN: (数字)9798331538606

ISBN: (纸本)9798331538613

Digital twins enable real-time modeling, simulation, and monitoring of complex systems, driving advancements in automation, robotics, and industrial applications. This study presents a large-scale digital twin-testing facility for evaluating mobile robots and pilot robotic systems in a research laboratory environment. The platform integrates high-fidelity physical and environmental models, providing a controlled yet dynamic setting for analyzing robotic behavior. A key feature of the system is its comprehensive data collection framework, capturing critical parameters such as position, orientation, and velocity, which can be leveraged for machine learning, performance optimization, and decision-making. The facility also supports the simulation of discrete operational systems, using predictive modeling to bridge informational gaps when real-time data updates are unavailable. The digital twin was validated through a matrix manufacturing system simulation, with an Augmented Reality (AR) interface on the HoloLens 2 to overlay digital information onto mobile platform controllers, enhancing situational awareness. The main contributions include a digital twin framework for deploying data-driven robotic systems and three key AR/VR integration optimization methods. Demonstrated in a laboratory setting, the system is a versatile tool for research and industrial applications, fostering insights into robotic automation and digital twin scalability while reducing costs and risks associated with real-world testing.

关键词： Service robots Navigation Metaverse Databases Scalability Digital representation Real-time systems Digital twins Mobile robots Robots

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Distributed Event-Triggered Bandit Convex Optimization With Time-Varying Constraints

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IEEE Transactions on Control of Network systems 2025年

作者： Zhang, Kunpeng Yi, Xinlei Wen, Guanghui Cao, Ming Johansson, Karl H. Chai, Tianyou Yang, Tao Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China Ministry of Education Shanghai Institute of Intelligent Science and Technology National Key Laboratory of Autonomous Intelligent Unmanned Systems Frontiers Science Center for Intelligent Autonomous Systems Shanghai201210 China Southeast University School of Automation Nanjing210096 China University of Groningen Engineering and Technology Institute Groningen Faculty of Science and Engineering GroningenAG 9747 Netherlands KTH Royal Institute of Technology Division of Decision and Control Systems School of Electrical Engineering and Computer Science Stockholm10044 Sweden KTH Royal Institute of Technology Digital Futures Stockholm10044 Sweden

This paper considers the distributed bandit convex optimization problem with time-varying inequality constraints over a network of agents, where the goal is to minimize network regret and cumulative constraint violation. Existing distributed online algorithms solving this problem require that each agent broadcasts its decision to its neighbors at each iteration. However, communication resources are often limited. To better utilize communication resources, we propose a distributed event-triggered online primal-dual algorithm with two-point bandit feedback. Under several classes of appropriately chosen decreasing parameter sequences and non-increasing event-triggered threshold sequences, we establish dynamic network regret and network cumulative constraint violation bounds. These bounds are comparable to the results achieved by distributed event-triggered online algorithms with full-information feedback. Finally, a numerical example is provided to verify the theoretical results. © 2014 IEEE.

关键词： Optimization algorithms

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Enhanced Glaucoma Classification Through Advanced Segmentation by Integrating Cup-to-Disc Ratio and Neuro-Retinal Rim Features

SSRN

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SSRN 2025年

作者： Pannu, Rabia Zubair, Muhammad Owais, Muhammad Hassan, Shoaib Umair, Muhammad Usman, Syed Muhammad Al-Bashrawi, Mousa Ahmed Hussain, Irfan Faculty of Information Technology and Computer Science University of Central Punjab Lahore Pakistan Interdisciplinary Research Center for Finance and Digital Economy King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia Department of Mechanical Nuclear Engineering Khalifa University Abu Dhabi United Arab Emirates School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China Department of Computer Science Bahria School of Engineering and Applied Sciences Bahria University Islamabad Pakistan Department of Information Systems and Operations Management King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia

Glaucoma is a progressive eye condition caused by high intraocular fluid pressure, damaging the optic nerve, leading to gradual, irreversible vision loss, often without noticeable symptoms. Subtle signs such as mild redness in the eyes, slightly blurred vision, and occasional headaches or eye pain may go unnoticed, earning it the nickname "silent thief of sight." Its prevalence is increasing with an aging population, driven by advances in healthcare care and increased life expectancy. Most computer-aided diagnosis (CAD) systems rely on the cup-to-disc ratio (CDR) for the diagnosis of glaucoma. This study introduces a novel approach by integrating CDR with the neuro-retinal rim ratio (NRR), which quantifies rim thickness within the optic disc (OD). NRR improves diagnostic accuracy by recording additional head changes of the optic nerve, such as rim thinning and tissue loss, which were overlooked using CDR alone. We proposed a modified ResUNet architecture for OD and optic cup (OC) segmentation, combining residual learning and U-Net to capture spatial context for semantic segmentation. Its performance was evaluated on the DRISHTI-GS and RIM-ONE datasets. For OC segmentation, the model achieved Dice Coefficient (DC) scores of 0.942 and 0.872 and Intersection over Union (IoU) values of 0.891 and 0.773 for DRISHTI-GS and RIM-ONE, respectively. For OD segmentation, the model achieved DC of 0.972 and 0.950 and IoU values of 0.945 and 0.940 for DRISHTI-GS and RIM-ONE, respectively. CDR and NRR were calculated from segmentation masks and used to train an SVM with a radial basis function, classifying the eyes as healthy or glaucomatous. The model achieved accuracies of 0.969 on DRISHTI-GS and 0.977 on RIM-ONE. © 2025, The Authors. All rights reserved.

关键词： Semantic Segmentation

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A study of different machine learning approaches for relation discovery task

A study of different machine learning approaches for relatio...

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International Conference on Energy, Power, Environment, Control and Computing (ICEPECC 2025)

作者： Fatima N. Al-Aswadi Wafa’ Za’al Alma’aitah Huah Yong Chan Keng Hoon Gan Zahid Iqbal Ahlam Al-Dhamari Institute of Computer Science and Digital Innovation UCSI University Kuala Lumpur 56000 Malaysia Department of Intelligence Systems Faculty of Artificial Intelligence Al-Balqa Applied University Salt 19117 Jordan School of Computer Sciences Universiti Sains Malaysia 11800 Gelugor Pulau Pinang Malaysia Department of Computer Science Air University Kharian Pakistan Department of Electrical and Computer Engineering Curtin University Malaysia Miri 98009 Sarawak Malaysia

ISBN: (数字)9781837243150

Relation extraction and classification, collectively referred to as relation discovery, are fundamental tasks in the ontology construction process. In recent years, numerous studies have dedicated their efforts to devising hybrid approaches, supporting inclusive innovation in computational linguistics and ontology engineering. These approaches merge traditional methods with Machine Learning (ML) or Deep Learning (DL) techniques to tackle relation discovery tasks. This research examines the applicability and efficacy of four distinct models—Support Vector Machine (SVM), Naive Bayes (NB), k-Nearest Neighbors (KNN), and Convolutional Neural Network (CNN)—in relation extraction and classification. Two different datasets, ACL RD-TEC 2.0 and ACLRelAcS, are utilized, alongside two distinct text representation methodologies, BOW and TF-IDF-BOW. An in-depth analysis of the results, including the advantages and limitations of each model, is presented in the results and discussion sections.

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From Competition to Classroom: A Hands-on Approach to Robotics Learning

From Competition to Classroom: A Hands-on Approach to Roboti...

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IEEE International Conference on Autonomous Robot systems and Competitions (ICARSC)

作者： Maria S. Lopes Jorge Diogo Ribeiro António Paulo Moreira Claudia D. Rocha João G. Martins José Maria Sarmento José Pedro Carvalho Paulo G. Costa Ricardo B. Sousa Faculty of Engineering University of Porto (FEUP) Porto Portugal INESC TEC - Institute for Systems and Computer Engineering Technology and Science Porto Portugal University of Tras-os-Montes and Alto Douro (UTAD) Vila Real Portugal SYSTEC-ARISE Research Center for System and Technologies Porto Portugal

ISBN: (数字)9798331538606

ISBN: (纸本)9798331538613

Robotics education plays a crucial role in developing STEM skills. However, university-level courses often emphasize theoretical learning, which can lead to decreased student engagement and motivation. In this paper, we tackle the challenge of providing hands-on robotics experience in higher education by adapting a mobile robot originally designed for competitions to be used in laboratory classes. Our approach integrates real-world robot operation into coursework, bridging the gap between simulation and physical implementation while maintaining accessibility. The robot's software is developed using ROS, and its effectiveness is assessed through student surveys. The results indicate that the platform increases student engagement and interest in robotics topics. Furthermore, feedback from teachers is also collected and confirmed that the platform boosts students' confidence and understanding of robotics.

关键词： Surveys Education Software Mobile robots Robots Autonomous robots

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Fuzzy Granule Density-Based Outlier Detection with Multi-Scale Granular Balls

arXiv

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arXiv 2025年

作者： Gao, Can Tan, Xiaofeng Zhou, Jie Ding, Weiping Pedrycz, Witold The College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen518060 China The National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China The School of Artificial Intelligence and Computer Science Nantong University Nantong226019 China The Faculty of Data Science City University of Macau Macau999078 China The Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Systems Research Institute Polish Academy of Sciences Warsaw Poland The Department of Electrical and Computer Engineering King Abdulaziz University Jeddah Saudi Arabia The Department of Computer Engineering Istinye University Istanbul Turkey

Outlier detection refers to the identification of anomalous samples that deviate significantly from the distribution of normal data and has been extensively studied and used in a variety of practical tasks. However, most unsupervised outlier detection methods are carefully designed to detect specified outliers, while real-world data may be entangled with different types of outliers. In this study, we propose a fuzzy rough sets-based multi-scale outlier detection method to identify various types of outliers. Specifically, a novel fuzzy rough sets-based method that integrates relative fuzzy granule density is first introduced to improve the capability of detecting local outliers. Then, a multi-scale view generation method based on granular-ball computing is proposed to collaboratively identify group outliers at different levels of granularity. Moreover, reliable outliers and inliers determined by the three-way decision are used to train a weighted support vector machine to further improve the performance of outlier detection. The proposed method innovatively transforms unsupervised outlier detection into a semi-supervised classification problem and for the first time explores the fuzzy rough sets-based outlier detection from the perspective of multi-scale granular balls, allowing for high adaptability to different types of outliers. Extensive experiments carried out on both artificial and UCI datasets demonstrate that the proposed outlier detection method significantly outperforms the state-of-the-art methods, improving the results by at least 8.48% in terms of the Area Under the ROC Curve (AUROC) index. The source codes are released at https://***/Xiaofeng-Tan/MGBOD. © 2025, CC BY.

关键词： Granulation

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Chatbots and Customer Service: Enhancing Marketing Management with AI Technologies

Chatbots and Customer Service: Enhancing Marketing Managemen...

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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Rajchandar, K. Hari Krishna, Somanchi Khalil Wani, Showkat Pathak, Supriya Jain, Amit Kumar Naveen Kumar, C.M. Sr University School of Cs & Ai Telangana Warangal India Vignana Bharathi Institute of Technology Department of Business Management Telangana State 501301 India Saudi Electronic University College of Administrative and Financial Sciences Department of Business Administration Riyadh Saudi Arabia Oriental University Faculty of Management and Commerce Madhya Pradesh Indore India Poornima University Department of Electrical & Electronics Engineering Rajasthan JaipurIS-2027-2031 India Malnad College of Engineering Department of Computer Science & Business Systems Karnataka Hassan573202 India

ISBN: (纸本)9798331508685

By increasing engagement, expediting client contacts, and boosting satisfaction through AI-driven solutions, chatbot integration in customer service has revolutionised marketing management. With an emphasis on how model assessment techniques - such as accuracy, precision, recall, and F1 score - can be applied to enhance chatbot efficacy in customer service scenarios, this paper investigates the role of machine learning models in optimising chatbot performance. In order to appropriately meet a variety of consumer demands, this research offers perspectives into the selection, training, and evaluation of chatbot models by analysing a range of AI technologies, such as natural language processing (NLP) and predictive analytics. The influence of chatbots on customer loyalty and retention is highlighted in this article, which also explores the benefits and limitations of utilising AI in real-time consumer interactions. According to research, well-considered ML models can produce more precise and sympathetic answers, which enhances brand perception and opens up new channels for CRM in marketing plans. © 2025 IEEE.

关键词： Sales

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Practical Quantum Circuit Implementation for Simulating Coupled Classical Oscillators

arXiv

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arXiv 2025年

作者： Luangsirapornchai, Natt Sanglaor, Peeranat Sornsaeng, Apimuk Bressan, Stéphane Chotibut, Thiparat Suksen, Kamonluk Chongstitvatana, Prabhas Department of Computer Engineering Faculty of Engineering Chulalongkorn University Bangkok10330 Thailand Chula Intelligent and Complex Systems Lab Department of Physics Faculty of Science Chulalongkorn University Bangkok10330 Thailand Science Mathematics and Technology Cluster Singapore University of Technology and Design 8 Somapah Road 487372 Singapore School of Computing National University of Singapore 13 Computing Drive Singapore117417 Singapore

Simulating large-scale coupled-oscillator systems presents substantial computational challenges for classical algorithms, particularly when pursuing first-principles analyses in the thermodynamic limit. Motivated by the quantum algorithm framework proposed by Babbush et al. [1], we present and implement a detailed quantum circuit construction for simulating one-dimensional spring-mass systems. Our approach incorporates key quantum subroutines, including block encoding, quantum singular value transformation (QSVT), and amplitude amplification, to realize the unitary time-evolution operator associated with simulating classical oscillators dynamics. In the uniform spring-mass setting, our circuit construction requires a gate complexity of (formula presented), where N is the number of oscillators and Ε is the target accuracy of the approximation. For more general, heterogeneous spring-mass systems, the total gate complexity is O(N log2 N log2(1/Ε)). Both settings require O(log2 N) qubits. Numerical simulations agree with classical solvers across all tested configurations, indicating that this circuit-based Hamiltonian simulation approach can substantially reduce computational costs and potentially enable larger-scale many-body studies on future quantum hardware. © 2025, CC0.

关键词： Hamiltonians

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Enhancing Building Safety: A Brief Review of Agent-Based Modeling for Fire Evacuation Simulation 16

Enhancing Building Safety: A Brief Review of Agent-Based Mod...

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16th International Conference on Ambient systems, Networks and Technologies Networks, ANT 2025 / 8th International Conference on Emerging Data and Industry 4.0, EDI40 2025

作者： Kasereka, Selain K. Kabwe, Ortega M. Kinyanta, Maria W.K. Kasongo, Audrey L. Ilunga, Godwill W.K. Muhambya, Katya Tashev, Tasho Kyamakya, Kyandoghere Mathematics Statistics and Computer Science Department University of Kinshasa Kinshasa Democratic Republic Congo Faculty of English Engineering Studies Technical University of Sofia Sofia Bulgaria ABIL Research Center Kinshasa Democratic Republic Congo Faculty of Computer Science Université Nouveaux Horizons Lubumbashi Democratic Republic Congo Ganpat University Faculty of Computer Application Gujarat India Université Libre des Pays des Grands Lacs Faculty of Sciences and Technology Goma Democratic Republic Congo Institute of Smart Systems Technologies University of Klagenfurt Klagenfurt Austria Polytechnic Faculty University of Kinshasa Kinshasa Democratic Republic Congo

The issue of building evacuation in the event of a fire is a significant concern in urban planning and architecture. In the absence of appropriate measures, an emergency situation can potentially result in disastrous outcomes. In this review article, we explore the application of agent-based modeling (ABM) in the simulation of building evacuations in the event of a free breakout. To address this issue, we present a synthesis of findings from several studies, highlighting the advantages of ABM in modeling complex evacuation dynamics such as crowd behavior, consideration of multiple parameters, and decision-making processes. Additionally, we provide insight into the simulation tools and techniques used in studies that demonstrate the practical applications of ABM in enhancing evacuation strategies. Furthermore, we identify current challenges, research gaps, and propose future directions to enhance the accuracy and effectiveness of fire evacuation modeling and simulations. This study aims to contribute to the improvement of proposed solutions and models for the development of successful, effective, and reliable evacuation plans to enhance the safety of occupants in buildings. © 2025 The Author(s).

关键词： Agent-based Modeling Agent-Based Simulation Building Safety Disaster Evacuation Fire Breakout

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