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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Xia, Jiangnan Yang, Yu Shen, Jiaxing Wang, Senzhang Cao, Jiannong School of Computer Science and Engineering Central South University China Centre for Learning Teaching and Technology The Education University of Hong Kong Hong Kong School of Data Science Lingnan University Hong Kong The Department of Computing The Hong Kong Polytechnic University Hong Kong

ISBN: (纸本)157735897X

Traffic prediction is pivotal in intelligent transportation systems. Existing works focus mainly on improving overall accuracy, overlooking a crucial problem of whether prediction results will lead to biased decisions by transportation authorities. In practice, the uneven deployment of traffic sensors in different urban areas produces imbalanced data, making the traffic prediction model fail in some urban areas and leading to unfair regional decision-making that eventually severely affects equity and quality of residents’ life. Existing fairness machine learning models struggle to maintain fair traffic prediction over prolonged periods. Although these models might achieve fairness at certain time slots, this static fairness will break down as traffic conditions change. To fill this research gap, we investigate prolonged fair traffic prediction, introducing two novel fairness metrics, i.e., region-based static fairness and sensor-based dynamic fairness, tailored to fairness fluctuations over time and across areas. An innovative prolonged fairness traffic prediction framework, namely FairTP, is then proposed. FairTP achieves prolonged fairness by alternating between “sacrifice” and “benefit” the prediction accuracy of each traffic sensor or area, ensuring that the number of these two actions are balanced over time. Specifically, FairTP incorporates a state identification module to discriminate whether the traffic sensors or areas are in a “sacrifice” or “benefit” state, thereby enabling prolonged fairness-aware traffic predictions. Additionally, we devise a state-guided balanced sampling strategy to select training examples to further enhance prediction fairness by mitigating the performance disparities among areas with uneven sensor distribution over time. Extensive experiments in two real-world datasets show that FairTP significantly improves prediction fairness without causing significant accuracy degradation. Copyright © 2025, Association for the Advancement of Ar

关键词： Traffic control

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The U-Net model application for retinal vessels segmentation using minimax approach

The U-Net model application for retinal vessels segmentation...

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2021 International Conference on Information and Digital Technologies, IDT 2021

作者： Martsenyuk, Vasyl Milian, Roksolana Milian, Nazar Department of Computer Science and Automatics University of Bielsko-Biala Bielsko-Biala Poland Department of Mathematics and Methods of its Teaching Ternopil Volodymyr Hnatiuk National Pedagogical University Ternopil Ukraine Department of Cybersecurity Ternopil Ivan Puluj National Technical University Ternopil Ukraine

ISBN: (纸本)9781665436922

In this article the implementation of neural network architecture based on a dense U-Net network is proposed. It is noted that retinal blood vessels are the basis for clinical diagnosis of some diseases. A review of the convolutional networks use for classification tasks and generalizion retinal vessel segmentation algorithms is performed. The general process of the neural network is presented. The differences between the real and the obtained results were evaluated. Evaluation of the neural network is carried out on several parameters. Indicators of binary cross-entropy and learning time when using different tile sizes are presented, based on these data determined by the solution to the problem of minimax ML for binary cross-entropy and learning time. The figure with the recognized blood vessels as a result of the model is presented. © 2020 IEEE computer Society. All rights reserved.

关键词： Machine learning

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The case for delegated AI autonomy for Human AI teaming in healthcare

arXiv

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arXiv 2025年

作者： Jia, Yan Evans, Harriet Porter, Zoe Graham, Simon McDermid, John Lawton, Tom Snead, David Habli, Ibrahim Department of Computer Science University of York York United Kingdom Histopathology Department University Hospitals Coventry and Warwickshire NHS Trust Coventry United Kingdom Warwick Medical School University of Warwick Coventry United Kingdom Histofy Ltd Coventry United Kingdom Improvement Academy Bradford Teaching Hospitals NHS Foundation Trust Bradford United Kingdom Department of Computer Science University of Warwick Coventry United Kingdom

In this paper we propose an advanced approach to integrating artificial intelligence (AI) into healthcare: autonomous decision support. This approach allows the AI algorithm to act autonomously for a subset of patient cases whilst serving a supportive role in other subsets of patient cases based on defined delegation criteria. By leveraging the complementary strengths of both humans and AI, it aims to deliver greater overall performance than existing human-AI teaming models. It ensures safe handling of patient cases and potentially reduces clinician review time, whilst being mindful of AI tool limitations. After setting the approach within the context of current human-AI teaming models, we outline the delegation criteria and apply them to a specific AI-based tool used in histopathology. The potential impact of the approach and the regulatory requirements for its successful implementation are then discussed. © 2025, CC BY-NC-SA.

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Structure-Aware DropEdge Towards Deep Graph Convolutional Networks

arXiv

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arXiv 2023年

作者： Han, Jiaqi Huang, Wenbing Rong, Yu Xu, Tingyang Sun, Fuchun Huang, Junzhou Department of Computer Science and Technology Tsinghua University Beijing China Gaoling School of Artificial Intelligence Renmin University of China Beijing Key Laboratory of Big Data Management and Analysis Methods Beijing China Tencent AI Lab Shenzhen China Department of Computer Science and Engineering The University of Texas Arlington United States

It has been discovered that Graph Convolutional Networks (GCNs) encounter a remarkable drop in performance when multiple layers are piled up. The main factor that accounts for why deep GCNs fail lies in over-smoothing, which isolates the network output from the input with the increase of network depth, weakening expressivity and trainability. In this paper, we start by investigating refined measures upon DropEdge—an existing simple yet effective technique to relieve over-smoothing. We term our method as DropEdge++ for its two structure-aware samplers in contrast to DropEdge: layer-dependent sampler and feature-dependent sampler. Regarding the layer-dependent sampler, we interestingly find that increasingly sampling edges from the bottom layer yields superior performance than the decreasing counterpart as well as DropEdge. We theoretically reveal this phenomenon with Mean-Edge-Number (MEN), a metric closely related to over-smoothing. For the feature-dependent sampler, we associate the edge sampling probability with the feature similarity of node pairs, and prove that it further correlates the convergence subspace of the output layer with the input features. Extensive experiments on several node classification benchmarks, including both full- and semi- supervised tasks, illustrate the efficacy of DropEdge++ and its compatibility with a variety of backbones by achieving generally better performance over DropEdge and the no-drop version. Copyright © 2023, The Authors. All rights reserved.

关键词： Drops

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User attitudes towards virtual home assessment technologies

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Journal of Medical Engineering and Technology 2022年第6期46卷 536-546页

作者： Lanfranchi, V. Jones, N. Read, J. Fegan, C. Field, B. Simpson, E. Revitt, C. Cudd, P. Ciravegna, F. Department of Computer Science The University of Sheffield Sheffield United Kingdom Sheffield Teaching Hospitals NHS Foundation Trust Sheffield United Kingdom NIHR Devices for Dignity MedTech Co-operative Affiliate Sheffield United Kingdom School of Health and Related Research University of Sheffield Sheffield United Kingdom Department Allied Health Professions Sheffield Hallam University Sheffield United Kingdom

Telehealth has long been highlighted as a way to solve issues of efficiency and effectiveness in healthcare and to improve patients’ care and has become fundamental to address patients’ needs during the COVID-19 pandemic;however previous studies have shown mixed results in the user acceptance of such technologies. Whilst many previous studies have focussed on clinical application of telehealth, we focus on the adoption of telehealth for virtual assessments visits aimed to evaluate the suitability of a property where a patient is discharged, and eventual adaptations needed. We present a study of stakeholders’ attitudes towards such virtual assessment visits. The study has been carried out with healthcare professionals and patients and allowed us to identify user attitudes, barriers and facilitators for the success of virtual assessment visits from the point of view of healthcare professionals and patients. Finally, we discuss implications for designers of telehealth services and guidelines that can be derived from our study. © 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

关键词： COVID-19

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Comment Analyzer: A Tool for Analyzing Comment Sets and Thread Structures of News Articles

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IEEE Transactions on Visualization and computer Graphics 2025年 PP卷 PP页

作者： Kiesel, Dora Riehmann, Patrick Engelmann, Ines Ramezani, Hanna Froehlich, Bernd Bauhaus Universitat Weimar Virtual Reality and Visualization Group Germany Jönköping University Computer Science and Informatics Department School of Engineering Sweden Friedrich Schiller University Jena Empirical Methods of Communication Science Germany German Federal Agency for Civic Education Germany Bauhaus-Universitat Weimar Virtual Reality and Visualization Group Germany

The lack of visually guided data exploration tools limits the scope of research questions communication scientists are able to study. The Comment Analyzer steps in where traditional statistical tools fail when it comes to researching the commenting behavior of news article readers. The basis of such an analysis are comment-thread corpora in which comments are tagged with various deliberative quality indicators as well as political stance. Our analysis tool provides a visual querying system for the exploration and analysis of such corpora and allows social scientists to gain insights into the distributions and relations between comment attributes, the homogeneity of thread sets, frequent thread structures and changes in comment qualities over the course of a single but in particular of multiple threads at once. We developed the tool in close collaboration with communication scientists in a user-centered approach. The system has proven its utility in thorough reviews with the communication scientists, by corroborating existing findings in the literature but particularly by provoking and answering new research questions. Final reviews with five independent experts confirmed these observations and revealed the potential of the Comment Analyzer for other datasets currently being created and analyzed in the communication sciences © 1995-2012 IEEE.

关键词： Graph Visualization Information Visualization Multi-Attribute Aggregation Set-based Visualization Text Visualization Tree Aggregation Visual Text Analytics

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Algorithm for Solving Inverse Problems of Geoelectrical Prospecting for Vertical Electrical Sounding by the Indirect Near-Boundary Element Method

Algorithm for Solving Inverse Problems of Geoelectrical Pros...

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International computer sciences and Information Technologies (CSIT)

作者： Liubov Zhuravchak Nataliya Zabrodska Software Department of Institute of Computer Science and Information Technologies Lviv Polytechnic National University Lviv Ukraine Department of Geoelectromagnetic Methods of Carpathian Branch of S.I. Subbotin Institute of Geophysics National Academy of Sciences Lviv Ukraine

ISBN: (数字)9798331542634

ISBN: (纸本)9798331542641

using the indirect near-boundary element method, an algorithm for recognizing the physical and geometric parameters of the inclusion in a piecewise homogeneous half-plane, whose components are in ideal contact, based on the potential field data measured at its boundary, is constructed. The direct and inverse two-dimensional problems of the potential theory in relation to electrical prospecting, in particular, the method of vertical electrical soundings, have been solved. A computational experiment was conducted in which initial approximations for the electrical conductivity of the inclusion, its center of mass, orientation, and dimensions were determined by the nature of the change in apparent resistivity. The inverse problem is solved using excess boundary conditions by a number of iterations while minimizing the functional.

关键词： Electric potential Inverse problems Shape Groundwater Contacts Conductivity Soil Rocks Water pollution Electromagnetics

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A memetic dynamic coral reef optimisation algorithm for simultaneous training, design, and optimisation of artificial neural networks (vol 14, 6961, 2024)

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SCIENTIFIC REPORTS 2024年第1期14卷 1-22页

作者： Berchez-Moreno, Francisco Duran-Rosal, Antonio M. Martinez, Cesar Hervas Gutierrez, Pedro A. Fernandez, Juan C. Department of Computer Science and Numerical Analysis University of Córdoba Córdoba Spain Maimonides Biomedical Research Institute of Córdoba IMIBIC University of Córdoba 14071 Córdoba Spain Department of Quantitative Methods Universidad Loyola Andalucía Córdoba Spain

Artificial Neural Networks (ANNs) have been used in a multitude of real-world applications given their predictive capabilities, and algorithms based on gradient descent, such as Backpropagation (BP) and variants, are usually considered for their optimisation. However, these algorithms have been shown to get stuck at local optima, and they require a cautious design of the architecture of the model. This paper proposes a novel memetic training method for simultaneously learning the ANNs structure and weights based on the Coral Reef Optimisation algorithms (CROs), a global-search metaheuristic based on corals’ biology and coral reef formation. Three versions based on the original CRO combined with a Local Search procedure are developed: (1) the basic one, called Memetic CRO; (2) a statistically guided version called Memetic SCRO (M-SCRO) that adjusts the algorithm parameters based on the population fitness; (3) and, finally, an improved Dynamic Statistically-driven version called Memetic Dynamic SCRO (M-DSCRO). M-DSCRO is designed with the idea of improving the M-SCRO version in the evolutionary process, evaluating whether the fitness distribution of the population of ANNs is normal to automatically decide the statistic to be used for assigning the algorithm parameters. Furthermore, all algorithms are adapted to the design of ANNs by means of the most suitable operators. The performance of the different algorithms is evaluated with 40 classification datasets, showing that the proposed M-DSCRO algorithm outperforms the other two versions on most of the datasets. In the final analysis, M-DSCRO is compared against four state-of-the-art methods, demonstrating its superior efficacy in terms of overall accuracy and minority class performance.

关键词： Artificial neural networks Classification Coral reef optimisation algorithm Local search Neuroevolution Robust estimators

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An Automatic CNN-based Face Mask Detection Algorithm Tested During the COVID-19 Pandemics

An Automatic CNN-based Face Mask Detection Algorithm Tested ...

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2022 International Conference of Yearly Reports on Informatics, Mathematics, and Engineering, ICYRIME 2022

作者： De Magistris, Giorgio Iacobelli, Emanuele Brociek, Rafal Napoli, Christian Department of Computer Control and Management Engineering Sapienza University of Rome Via Ariosto 25 Roma00185 Italy Department of Mathematics Applications and Methods for Artificial Intelligence Faculty of Applied Mathematics Silesian University of Technology Gliwice44-100 Poland Institute for Systems Analysis and Computer Science Italian National Research Council Via dei Taurini 19 Roma00185 Italy

The ongoing COVID-19 pandemic has highlighted the importance of wearing face masks as a preventive measure to reduce the spread of the virus. In medical settings, such as hospitals and clinics, healthcare professionals and patients are required to wear surgical masks for infection control. However, the use of masks can hinder facial recognition technology, which is commonly used for identity verification and security purposes. In this paper, we propose a convolutional neural network (CNN) based approach to detect faces covered by surgical masks in medical settings. We evaluated the proposed CNN model on a test set comprising of masked and unmasked faces. The results showed that our model achieved an accuracy of over 96% in detecting masked faces. Furthermore, our model demonstrated robustness to different mask types and fit variations commonly encountered in medical settings. Our approaches reaches state of the art results in terms of accuracy and generalization. © 2022 Copyright for this paper by its authors.

关键词： COVID-19

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Instrumental Variables in Causal Inference and Machine Learning: A Survey

arXiv

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arXiv 2022年

作者： Wu, Anpeng Kuang, Kun Xiong, Ruoxuan Wu, Fei The College of Computer Science and Technology Zhejiang University China The Department of Quantitative Theory & Methods Emory University United States

Causal inference is the process of using assumptions, study designs, and estimation strategies to draw conclusions about the causal relationships between variables based on data. This allows researchers to better understand the underlying mechanisms at work in complex systems and make more informed decisions. In many settings, we may not fully observe all the confounders that affect both the treatment and outcome variables, complicating the estimation of causal effects. To address this problem, a growing literature in both causal inference and machine learning proposes to use Instrumental Variables (IV). This paper serves as the first effort to systematically and comprehensively introduce and discuss the IV methods and their applications in both causal inference and machine learning. First, we provide the formal definition of IVs and discuss the identification problem of IV regression methods under different assumptions. Second, we categorize the existing work on IV methods into three streams according to the focus on the proposed methods, including two-stage least squares with IVs, control function with IVs, and evaluation of IVs. For each stream, we present both the classical causal inference methods, and recent developments in the machine learning literature. Then, we introduce a variety of applications of IV methods in real-world scenarios and provide a summary of the available datasets and algorithms. Finally, we summarize the literature, discuss the open problems and suggest promising future research directions for IV methods and their applications. We also develop a toolkit of IVs methods reviewed in this survey at https://***/causal-machine-learning-lab/mliv. Copyright © 2022, The Authors. All rights reserved.

关键词： Machine learning

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