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35th Irish systems and Signals Conference, ISSC 2024

作者： Hassard, Peter Kerr, Dermot School of Computing Engineering and Intelligent Systems Ulster University United Kingdom

ISBN: (纸本)9798350352986

This paper demonstrates how supervised machine learning algorithms can be leveraged to predict match outcomes from 2015/16 football season for matches in the top professional leagues of 5 countries (Spain - La Liga, England - Premier League, Italy - Serie A, Germany - 1. Bundesliga and France - Ligue 1). In the summer of 2023 StatsBomb released this free data;once important features on game play activity are generated and processed. A clear correlation between the match outcome and the location of a team's events on the pitch will be shown, moreover the ability to predict a match outcome based on the team's previous matches, this was done using their mean values for relevant features. For optimal prediction levels, the machine learning models in this paper had their hyperparameters selectively adjusted. © 2024 IEEE.

关键词： Forecasting

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Adaptive Fixed-Time Synchronization of Delayed Memristor-Based Neural Networks with Discontinuous Activations

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Computer Modeling in Engineering & Sciences 2023年第1期134卷 221-239页

作者： Tianyuan Jia Xiangyong Chen Xiurong Yao Feng Zhao Jianlong Qiu School of Automation and Electrical Engineering and Key Laboratory of Complex Systems and Intelligent Computing in Universities of ShandongLinyi UniversityLinyi276005China The Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems and School of AutomationChina University of GeosciencesWuhan430074China

Fixed-time synchronization(FTS)of delayed memristor-based neural networks(MNNs)with discontinuous activations is studied in this *** continuous and discontinuous activations are considered *** the mixed delays which are closer to reality are taken into the ***,two kinds of control schemes are proposed,including feedback and adaptive control *** on some lemmas,mathematical inequalities and the designed controllers,a few synchronization criteria are ***,the upper bound of settling time(ST)which is independent of the initial values is ***,the feasibility of our theory is attested by simulation examples.

关键词： Memristor-based neural networks fixed-time synchronization mixed delays discontinuous activations adaptive control

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Uncertainty-aware Superpoint Graph Transformer for Weakly Supervised 3D Semantic Segmentation

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IEEE Transactions on Fuzzy systems 2025年

作者： Fan, Yan Wang, Yu Zhu, Pengfei Hui, Le Xie, Jin Hu, Qinghua Tianjin University College of Intelligence and Computing Tianjin300350 China Ministry of Education of the People's Republic of China Engineering Research Center of City Intelligence and Digital Governance China Haihe Laboratory of Itai Tianjin China Northwestern Polytechnical University School of Electronics and Information Shaanxi Key Laboratory of Information Acquisition and Processing China Nanjing University of Science and Technology Pca Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education China Nanjing University State Key Laboratory for Novel Software Technology Nanjing China

Weakly supervised 3D semantic segmentation has successfully mitigated the labor-intensive and time-consuming task of annotating 3D point clouds. However, reliably utilizing the minimal point-wise annotations for unlabeled data in complex and large-scale scenes is still challenging, such as only 20 points labeled in 2 million points. To tackle this challenge, we propose a new Uncertainty-aware Superpoint Graph Transformer (UaSGT) framework that utilizes minimal annotations for unlabeled data learning through reliable long-range supervision propagation from labeled superpoints to unlabeled superpoints. First, we propose a superpoint graph transformer to achieve long-range supervision propagation along the attention-based fuzzy subsets defined on superpoints. The attention-based fuzzy subset measures the membership of unlabeled superpoints to clusters centered on labeled superpoints. Second, we employ an uncertainty-aware membership rectification technique on the fuzzy subset to ensure reliable propagation among superpoints within the same category. This technique integrates an uncertainty prediction module to mask the influence of unreliable membership and a spatial prior refinement module to reduce uncertainty in intra-class membership degrees. Finally, experimental results on two large-scale benchmarks S3DIS and ScanNet-V2 demonstrate the superiority of our approach compared to the state-of-the-art with at least 90% annotation reduction, and our method also achieves comparable performance to fully supervised methods with less than 0.1% labeled points. Our code is available at https://***/fanyan0411/UaSGT. © 2025 IEEE.

关键词： Semantic Segmentation

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Towards Safe AI Clinicians: A Comprehensive Study on Large Language Model Jailbreaking in Healthcare

arXiv

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arXiv 2025年

作者： Zhang, Hang Lou, Qian Wang, Yanshan Intelligent Systems Program School of Computing and Information University of Pittsburgh PA United States Department of Computer Science University of Central Florida OrlandoFL United States Department of Health Information Management University of Pittsburgh PA United States Department of Biomedical Informatics University of Pittsburgh PA United States Clinical and Translational Science Institute University of Pittsburgh PittsburghPA United States University of Pittsburgh Medical Center PittsburghPA United States

Large language models (LLMs) are increasingly utilized in healthcare applications. However, their deployment in clinical practice raises significant safety concerns, including the potential spread of harmful information. This study systematically assesses the vulnerabilities of seven LLMs to three advanced black-box jailbreaking techniques within medical contexts. To quantify the effectiveness of these techniques, we propose an automated and domain-adapted agentic evaluation pipeline. Experiment results indicate that leading commercial and open-source LLMs are highly vulnerable to medical jailbreaking attacks. To bolster model safety and reliability, we further investigate the effectiveness of Continual Fine-Tuning (CFT) in defending against medical adversarial attacks. Our findings underscore the necessity for evolving attack methods evaluation, domain-specific safety alignment, and LLM safety-utility balancing. This research offers actionable insights for advancing the safety and reliability of AI clinicians, contributing to ethical and effective AI deployment in healthcare. © 2025, CC BY-NC-SA.

关键词： Patient treatment

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A Finite Element Blob Detector for Robust Features

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16th International Conference on Image Analysis and Processing (ICIAP)

作者： Kerr, Dermot Coleman, Sonya Scotney, Bryan School of Computing and Intelligent Systems University of Ulster Magee United Kingdom School of Computing and Information Engineering University of Ulster Coleraine United Kingdom

ISBN: (纸本)9783642240850

Traditionally feature extraction is focussed on edge and corner detection, however, more recently points of interest and blob like features have also become prominent in the field of computer vision and are typically used to determine correspondences between two images of the same scene. We present a new approach to a Hessian blob detector, designed within the finite element framework, which is similar to the multi-scale approach applied in the SURF detector. We present performance evaluation that demonstrates the accuracy of our approach in comparison to well known existing algorithms.

关键词： blob detector finite element framework

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Simplified prescribed performance tracking control of uncertain nonlinear systems

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Science China(Information Sciences) 2022年第8期65卷 291-292页

作者： Quan-Yong FAN Shuoheng XU Bin XU Jianlong QIU School of Automation Northwestern Polytechnical University School of Automation and Electrical Engineering Linyi University Key Laboratory of Complex Systems and Intelligent Computing in Universities of Shandong Linyi University

Dear editor,In practical engineering, some certain transient and steady performance, such as overshoot, adjustment time, and steady-state error, need to meet specific requirements. In order to ensure that the system s...

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Biologically Motivated Feature Extraction

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16th International Conference on Image Analysis and Processing (ICIAP)

作者： Coleman, Sonya Scotney, Bryan Gardiner, Bryan School of Computing and Intelligent Systems University of Ulster Magee United Kingdom School of Computing and Information Engineering University of Ulster Coleraine United Kingdom

ISBN: (纸本)9783642240850

We present a biologically motivated approach to fast feature extraction on hexagonal pixel based images using the concept of eye tremor in combination with the use of the spiral architecture and convolution of non-overlapping Laplacian masks. We generate seven feature maps "a-trous" that can be combined into a single complete feature map, and we demonstrate that this approach is significantly faster than the use of conventional spiral convolution or the use of a neighbourhood address look-up table on hexagonal images.

关键词： hexagonal images feature extraction spiral architecture eye tremor

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Optimizing Coverage Performance of Multiple Random Path-planning Robots

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Paladyn 2012年第1期3卷 11-22页

作者： Habib, Md Ahsan Alam, M.S. Siddique, N.H. Department of Applied Physics Electronics and Communication Engineering University of Dhaka Bangladesh School of Computing and Intelligent Systems University of Ulster United Kingdom

This paper presents a new approach to the multi-agent coverage path-planning problem. An efficient multi-robot coverage algorithm yields a coverage path for each robot, such that the union of all paths generates an almost full coverage of the terrain and the total coverage time is minimized. The proposed algorithm enables multiple robots with limited sensor capabilities to perform efficient coverage on a shared territory. Each robot is assigned to an exclusive route which enables it to carry out its tasks simultaneously, e.g., cleaning assigned floor area with minimal path overlapping. It is very difficult to cover all free space without visiting some locations more than once, but the occurrence of such events can be minimized with efficient algorithms. The proposed multi-robot coverage strategy directs a number of simple robots to cover an unknown area in a systematic manner. This is based on footprint data left by the randomized path-planning robots previously operated on that area. The developed path-planning algorithm has been applied to a simulated environment and robots to verify its effectiveness and performance in such an application. © Md Ahsan Habib et al. 2012.

关键词： Multiobjective optimization

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Exploring the Potential of Large Language Models for Estimating the Reading Comprehension Question Difficulty

arXiv

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arXiv 2025年

作者： Jain, Yoshee Hollander, John He, Amber Tang, Sunny Zhang, Liang Sabatini, John Siebel School of Computing and Data Science University of Illinois Urbana-Champaign UrbanaIL61801 United States Department of Psychology and Counseling Arkansas State University JonesboroAR72401 United States School of Computer Science Carnegie Mellon University PittsburghPA15213 United States Heinz College of Information Systems and Public Policy Carnegie Mellon University PittsburghPA15213 United States Department of Electrical and Computer Engineering University of Memphis MemphisTN38152 United States Institute for Intelligent Systems University of Memphis MemphisTN38152 United States Department of Psychology University of Memphis MemphisTN38152 United States

Reading comprehension is a key for individual success, yet the assessment of question difficulty remains challenging due to the extensive human annotation and large-scale testing required by traditional methods such as linguistic analysis and Item Response Theory (IRT). While these robust approaches provide valuable insights, their scalability is limited. There is potential for Large Language Models (LLMs) to automate question difficulty estimation;however, this area remains underexplored. Our study investigates the effectiveness of LLMs, specifically OpenAI’s GPT-4o and o1, in estimating the difficulty of reading comprehension questions using the Study Aid and Reading Assessment (SARA) dataset. We evaluated both the accuracy of the models in answering comprehension questions and their ability to classify difficulty levels as defined by IRT. The results indicate that, while the models yield difficulty estimates that align meaningfully with derived IRT parameters, there are notable differences in their sensitivity to extreme item characteristics. These findings suggest that LLMs can serve as the scalable method for automated difficulty assessment, particularly in dynamic interactions between learners and Adaptive Instructional systems (AIS), bridging the gap between traditional psychometric techniques and modern AIS for reading comprehension and paving the way for more adaptive and personalized educational assessments. Copyright © 2025, The Authors. All rights reserved.

关键词： Question answering

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Decentralized Adaptive Filtering for Multi-agent systems with Uncertain Couplings

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IEEE/CAA Journal of Automatica Sinica 2014年第1期1卷 101-112页

作者： Hongbin Ma Yini Lv Chenguang Yang Mengyin Fu the School of Automation Beijing Institute of Technology the Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology the School of Computing and Mathematics Plymouth University

In this paper, the problem of decentralized adaptive filtering for multi-agent systems with uncertain couplings is formulated and investigated. This problem is challenging due to the mutual dependency of state estimation and coupling estimation. First, the problem is divided into four typical types based on the origin of coupling relations and linearity of the agent dynamics. Then models of the four types are given and the corresponding decentralized adaptive filtering algorithms are designed for the purpose of estimation of the unknown states and couplings which denotes the relations between agents and their neighbor agents in terms of states or outputs simultaneously,with preliminary stability analysis and discussions. For testing the effects of algorithm, with the so-called certainty-equivalence principle, control signals are designed based on the results of state estimation and coupling estimation got by the proposed decentralized adaptive filtering algorithms. Extensive simulations are conducted to verify the effectiveness of considered algorithms..

关键词： Couplings Covariance matrices Multi-agent systems Adaptation models Kalman filters Mathematical model

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