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27th RoboCup International Symposium, 2024

作者： Abreu, Miguel Mota, Pedro Azevedo, Tomás Silva, Francisco Reis, Luís Paulo Lau, Nuno Florido, Mário LIACC/LASI/FEUP Artificial Intelligence and Computer Science Lab Faculty of Engineering of the University of Port University of Porto Porto Portugal LIACC/LASI/FCUP Artificial Intelligence and Computer Science Lab Faculty of Sciences of the University of Porto University of Porto Porto Portugal IEETA/LASI/DETI Institute of Electronics and Informatics Engineering of Aveiro Department of Electronics Telecommunications and Informatics University of Aveiro Aveiro Portugal

ISBN: (纸本)9783031858581

FC Portugal, a simulated humanoid soccer team from the universities of Porto and Aveiro, achieved the third consecutive victory in the main competition of the 2024 RoboCup 3D Simulation League. The team registered 16 wins, 3 ties, and 2 losses, scoring a total of 105 goals while only conceding 17. FC Portugal also ended first in two secondary events: the Scientific Challenge, which evaluates innovative scientific contributions within the league, and the Fat Proxy Challenge, where teams share a common set of low-level skills and compete for the best high-level strategy. The codebase used in this competition was developed in Python after RoboCup 2021. This paper presents a brief overview of its structure along with the new features developed for this year’s competition. The key developments include a hierarchical learning framework, presented at the Scientific Challenge, and the release of our codebase to the RoboCup community. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： 3D modeling

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Fingerprint Matching for Noisy and Distorted Patterns using a Siamese Network with ResNet50 and Multihead Attention

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IEEE Access 2025年 13卷 88047-88064页

作者： Sawhney, Tinny Sharma, Ashu Abrol, Pawanesh Lehana, Parveen Kumar Chaahat Yadav, Vaishali Scherer, Rafal Kulakov, Oleksii AlZubi, Ahmad Ali University of Jammu DSP Lab Department of Electronics India University of Jammu Department of Computer Science and IT India Chitkara University Institute of Engineering and Technology Chitkara University Department of Computer Science and Engineering Punjab India Manipal University Jaipur Jaipur India Czestochowa University of Technology Department of Artificial Intelligence Częstochowa Poland National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute" Faculty of Informatics and Software Engineering Kyiv Ukraine King Saud University Community College Department of Computer Science Riyadh Saudi Arabia

Dermatoglyphics, the study of unique ridge patterns on fingertips, plays a crucial role in fingerprint-based identification. However, skin conditions such as psoriasis, eczema, and verruca vulgaris can distort these patterns. This distortion creates challenges for fingerprint matching systems in forensic science and biometric authentication. This paper proposes a Siamese network with a modified ResNet50 architecture and multihead attention mechanisms to improve fingerprint matching under such distortions. The Siamese network enables the system to compare fingerprint pairs effectively, even under distortions. The modified ResNet50 architecture captures intricate ridge patterns, while the multihead attention mechanisms focus on critical fingerprint regions. Overall, the proposed approach enhances the system’s ability to learn discriminative features. As a result, it can effectively differentiate between matched and unmatched fingerprint pairs, even in the presence of moderate to extreme noise. The proposed system was trained on the Sokoto coventry fingerprint dataset (SOCOFing) and a custom dataset. Experimental results demonstrate high accuracy, with the system achieving 99.47% accuracy under low noise conditions and 90% under moderate noise. However, performance declined to 55.56% under extreme distortions. A comparative analysis highlighted the superiority of the proposed system over widely used minutiae-based methods. The latter exhibited a significant drop in matching scores, falling below 15% for highly distorted fingerprints. These findings underscore the limitations of traditional fingerprint recognition techniques and highlight the effectiveness of proposed approach in handling dermatoglyphic distortions. © 2013 IEEE.

关键词： Feature Selection

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Drone-Based Public Surveillance Using 3D Point Clouds and Neuro-Fuzzy Classifier

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computers, Materials & Continua 2025年第3期82卷 4759-4776页

作者： Yawar Abbas Aisha Ahmed Alarfaj Ebtisam Abdullah Alabdulqader Asaad Algarni Ahmad Jalal Hui Liu Faculty of Computing and AI Air UniversityIslamabad44000Pakistan Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Information Technology College of Computer and Information SciencesKing Saud UniversityRiyadh12372Saudi Arabia Department of Computer Sciences Faculty of Computing and Information TechnologyNorthern Border UniversityRafha91911Saudi Arabia Department of Computer Science and Engineering College of InformaticsKorea UniversitySeoul02841Republic of Korea Cognitive Systems Lab University of BremenBremen28359Germany

Human Activity Recognition(HAR)in drone-captured videos has become popular because of the interest in various fields such as video surveillance,sports analysis,and human-robot ***,recognizing actions from such videos poses the following challenges:variations of human motion,the complexity of backdrops,motion blurs,occlusions,and restricted camera *** research presents a human activity recognition system to address these challenges by working with drones’red-green-blue(RGB)*** first step in the proposed system involves partitioning videos into frames and then using bilateral filtering to improve the quality of object foregrounds while reducing background interference before converting from RGB to grayscale *** YOLO(You Only Look Once)algorithm detects and extracts humans from each frame,obtaining their skeletons for further *** joint angles,displacement and velocity,histogram of oriented gradients(HOG),3D points,and geodesic Distance are *** features are optimized using Quadratic Discriminant Analysis(QDA)and utilized in a Neuro-Fuzzy Classifier(NFC)for activity ***-world evaluations on the Drone-Action,Unmanned Aerial Vehicle(UAV)-Gesture,and Okutama-Action datasets substantiate the proposed system’s superiority in accuracy rates over existing *** particular,the system obtains recognition rates of 93%for drone action,97%for UAV gestures,and 81%for Okutama-action,demonstrating the system’s reliability and ability to learn human activity from drone videos.

关键词： Activity recognition geodesic distance pattern recognition neuro fuzzy classifier

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A Practically Scalable Approach to the Closest Vector Problem for Sieving via QAOA with Fixed Angles

arXiv

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arXiv 2025年

作者： Priestley, Ben Wallden, Petros Department of Computer Science University of Oxford Quantum Software Lab School of Informatics University of Edinburgh United Kingdom Quantum Software Lab School of Informatics University of Edinburgh United Kingdom

The NP-hardness of the closest vector problem (CVP) is an important basis for quantum-secure cryptography, in much the same way that integer factorisation’s conjectured hardness is at the foundation of cryptosystems like RSA. Recent work with heuristic quantum algorithms [1] indicates the possibility to find close approximations to (constrained) CVP instances that could be incorporated within fast sieving approaches for factorisation. This work explores both the practicality and scalability of the proposed heuristic approach to explore the potential for a quantum advantage for approximate CVP, without regard for the subsequent factoring claims. We also extend the proposal to include an antecedent "pre-training" scheme to find and fix a set of parameters that generalise well to increasingly large lattices, which both optimises the scalability of the algorithm, and permits direct numerical analyses. Our results further indicate a noteworthy quantum speed-up for lattice problems obeying a certain ‘prime’ structure, approaching fifth order advantage for QAOA of fixed depth p = 10 compared to classical brute-force, motivating renewed discussions about the necessary lattice dimensions for quantum-secure cryptosystems in the near-term. © 2025, CC BY.

关键词： Crystal lattices

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DRDM: A Disentangled Representations Diffusion Model for Synthesizing Realistic Person Images

DRDM: A Disentangled Representations Diffusion Model for Syn...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Huang, Enbo Zhang, Yuan Huang, Faliang Zhang, Guangyu Liu, Yang Guangxi Key Lab of Human-machine Interaction and Intelligent Decision Nanning Normal University Nanning China School of Computer and Information Engineering Nanning Normal University Nanning China College of Mathematics and Informatics South China Agricultural University Guangzhou China School of Computer Science and Engineering Sun Yat-sen University Guangzhou China

ISBN: (纸本)9798350368741

Person image synthesis with controllable body poses and appearances is an essential task owing to the practical needs in the context of virtual try-on, image editing and video production. However, existing methods face significant challenges with details missing, limbs distortion and the garment style deviation. To address these issues, we propose a Disentangled Representations Diffusion Model (DRDM) to generate photo-realistic images from source portraits in specific desired poses and appearances. First, a pose encoder is responsible for encoding pose features into a high-dimensional space to guide the generation of person images. Second, a body-part subspace decoupling block (BSDB) disentangles features from the different body parts of a source figure and feeds them to the various layers of the noise prediction block, thereby supplying the network with rich disentangled features for generating a realistic target image. Moreover, during inference, we develop a parsing map-based disentangled classifier-free guided sampling method, which amplifies the conditional signals of texture and pose. Extensive experimental results on the Deepfashion dataset demonstrate the effectiveness of our approach in achieving pose transfer and appearance control. The associated project can be found at https://***/lovemusiceb/DRDM. © 2025 IEEE.

关键词： Diffusion Model Disentangled Representation Human Parsing Person image synthesis

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Integrating Function and Connection: Two Perspectives to Telepresence Robot Design for Classroom 25

Integrating Function and Connection: Two Perspectives to Tel...

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2025 ACM International Conference on Supporting Group Work, GROUP Companion 2025

作者： Berhane, Roda Akiode, Deo Adejoke Joshi, Swapna Elmimouni, Houda School of Informatics Computing and Engineering Indiana University BloomingtonIN United States Field Robotics Lab Northeastern University BostonMA United States Department of Computer Science University of Manitoba WinnipegMB Canada Information Systems and New Media University of Siegen Siegen Germany

ISBN: (纸本)9798400711879

This study explores robotic telepresence in classrooms, from the perspectives of both the remote students (operators) and their in-person classmates. Analyzing a subset of field study data from 35 participants (22 operators, 13 classmates), we identified differing priorities: operators of the robot emphasize the need for improved functional aspects like camera control, while co-present classmates value social presence, interaction and identification, advocating for features like personalization and effective communication channels. These insights underline the need for telepresence robots that balance functional efficacy with social integration to provide a holistic educational experience. This work, though incomplete, enriches Group and CSCW literature on telepresence robots to facilitate collaborative interactions by highlighting the distinct needs of remote and in-person students, proposing design recommendations for telepresence robots that are technically proficient and socially responsive. © 2025 Copyright held by the owner/author(s).

关键词： Students

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A Mamdani Fuzzy Inference System for Comprehensive Diabetes Risk Evaluation

A Mamdani Fuzzy Inference System for Comprehensive Diabetes ...

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International Conference on Electrical, computer and Communication Engineering (ECCE)

作者： Tanjina Ahmed Tuly Tanjid Ahammed Shafin Md. Zahid Hasan Md. Hasan Imam Bijoy Mohammad Shamsul Arefin Mohammad Shorif Uddin Department of Computer Science and Engineering Health Informatics Research Lab Daffodil International University Dhaka Bangladesh Department of Computer Science and Engineering Chittagong University of Engineering and Technology Chattogram Bangladesh Department of Computer Science and Engineering Jahangirnagar University Savar Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Diabetes mellitus is a chronic condition characterized by insufficient insulin production and the inability to effectively utilize the insulin hormone, leading to elevated blood glucose levels. As diabetes is incurable and persists throughout a patient's lifetime, effective management requires early identification of risk factors to ensure continuous and feasible treatment. This study proposes a unique decision support system based on the Mamdani Fuzzy Inference System (MFIS) to predict diabetes risk. This study utilizes a publicly available diabetes dataset containing 442 data that includes nine key health metrics such as BMI, blood glucose, cholesterol levels, and blood pressure to predict the risk of diabetes. The dataset aims to support early diagnosis and better diabetes management by providing insights into individual risk factors. This resource contributes to the development of more accurate decision-support systems in healthcare. We identify key variables and establish a membership function to assess individual risk. The Fuzzy Inference System (FIS) is well-suited for handling complex and uncertain conditions, making it an effective tool for this application. The advantages of the MFIS include its ability to incorporate expert knowledge through linguistic variables and fuzzy rules. This system demonstrates the practical benefits of MFIS in developing intelligent decision-making tools, particularly in the domain of diabetes risk prediction.

关键词： Fuzzy logic Decision support systems Accuracy Uncertainty Predictive models Blood pressure Diabetes Glucose Insulin Blood

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Predicting Heat Stroke Risk: A Clinical Decision Support System using Fuzzy Association Mining Approach

Predicting Heat Stroke Risk: A Clinical Decision Support Sys...

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International Conference on Electrical, computer and Communication Engineering (ECCE)

作者： Abdullah Al Noman Rinvi Jaman Riti Md. Zahid Hasan Md. Hasan Imam Bijoy Mohammad Shorif Uddin Mohammad Shamsul Arefin Department of Computer Science and Engineering Health Informatics Research Lab Daffodil International University Dhaka Bangladesh Department of Computer Science and Engineering Jahangirnagar University Savar Bangladesh Department of Computer Science and Engineering Chittagong University of Engineering and Technology Chattogram Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Heat stroke is a serious medical condition that requires immediate treatment and is exacerbated by intense heat and climate change. This paper introduces a Clinical Decision Support System (CDSS) for heat stroke risk assessment based on Fuzzy Association Rule Mining. The system evaluates key attributes extracted from the Stanford Bioengineering Senior Capstone Project dataset, including daily water intake, cardiovascular history, heat index, environmental and rectal temperatures, blood pressure, pulse rate, humidity, age, and sex. These attributes help identify detailed patterns associated with heat stroke risk. Using fuzzy logic, the CDSS addresses the inherent vagueness of medical information through a set of "if- then" rules designed for healthcare practitioners. The system is tested with historical data, demonstrating its effectiveness in recognizing critical parameters to provide personalized, timely attention to potential heat stroke cases. It aims to reduce heat- related illnesses and fatalities by enabling rapid data-driven decision-making in healthcare. Despite certain limitations, this study highlights the necessity of intelligent systems for proactive health management.

关键词： Decision support systems Climate change Fuzzy systems Risk management Predictive models Medical services Medical conditions

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From Clicks to Conversations: Evaluating the Effectiveness of Conversational Agents in Statistical Analysis

arXiv

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arXiv 2025年

作者： Wen, Qifu Kochhar, Prishita Zeyada, Sherif Javaheri, Tahereh Rawassizadeh, Reza Department of Computer Science Boston University Metropolitan College United States Health Informatics Lab Boston University Metropolitan College United States

The rapid proliferation of data science forced different groups of individuals with different backgrounds to adapt to statistical analysis. We hypothesize that conversational agents are better suited for statistical analysis than traditional graphical user interfaces (GUI). In this work, we propose a novel conversational agent, StatZ, for statistical analysis. We evaluate the efficacy of StatZ relative to established statistical software—SPSS, SAS, Stata, and JMP—in terms of accuracy, task completion time, user experience, and user satisfaction. We combined the proposed analysis question from state-of-the-art language models with suggestions from statistical analysis experts and tested with 51 participants from diverse backgrounds. Our experimental design assessed each participant’s ability to perform statistical analysis tasks using traditional statistical analysis tools with GUI and our conversational agent. Results indicate that the proposed conversational agents significantly outperform GUI statistical software in all assessed metrics, including quantitative (task completion time, accuracy, and user experience), and qualitative (user satisfaction) metrics. Our findings underscore the potential of using conversational agents to enhance statistical analysis processes, reducing cognitive load and learning curves and thereby proliferating data analysis capabilities, to individuals with limited knowledge of *** Codes 62-07 Copyright © 2025, The Authors. All rights reserved.

关键词： Chatbots

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Learning in public goods games: the effects of uncertainty and communication on cooperation

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Neural Computing and Applications 2025年 1-34页

作者： Orzan, Nicole Acar, Erman Grossi, Davide Rădulescu, Roxana Bernoulli Institute for Mathematics Computer Science and Artificial Intelligence University of Groningen Nijenborgh Groningen9747 AG Netherlands Institute for Logic Language and Computation University of Amsterdam Science Park Amsterdam1098XH Netherlands Informatics Institute University of Amsterdam Science Park Amsterdam1098XH Netherlands Amsterdam Center for Law and Economics University of Amsterdam Nieuwe Achtergracht 166 Amsterdam1018 WV Netherlands Intelligent Systems Group Utrecht University Princetonplein 5 Utrecht3584 CC Netherlands Artificial Intelligence Lab Vrije Universiteit Brussel Pleinlaan 2 Brussels1050 Belgium

Communication is a widely used mechanism to promote cooperation in multi-agent systems. In the field of emergent communication, agents are typically trained in specific environments: cooperative, competitive or mixed-motive. Motivated by the idea that real-world settings are characterized by incomplete information and that humans face daily interactions under a wide spectrum of incentives, we aim to explore the role of emergent communication when simultaneously exploited across all these contexts. In this work, we pursue this line of research by focusing on social dilemmas. To do this, we developed an extended version of the Public Goods Game, which allows us to train independent reinforcement learning agents simultaneously in different scenarios where incentives are (mis)aligned to various extents. Additionally, agents experience uncertainty in terms of the alignment of their incentives with those of others. We equip agents with the ability to learn a communication policy and study the impact of emergent communication in the face of uncertainty among agents. Our findings show that in settings where all agents have the same level of uncertainty, communication can enhance the cooperation of the whole group. However, in cases of asymmetric uncertainty, the agents that do not face uncertainty learn to use communication to deceive and exploit their uncertain peers. © The Author(s) 2025.

关键词： Adversarial machine learning

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