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3rd International Workshop on Applications of Medical Artificial Intelligence, AMAI 2024 held in conjunction with the 27th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2024

作者： Li, Yanfeng Sun, Jinghan Wang, Liansheng Department of Computer Science at School of Informatics Xiamen University Xiamen China National Institute for Data Science in Health and Medicine Xiamen University Xiamen China

ISBN: (纸本)9783031820069

The utilization of Artificial Intelligence in automatically generating radiology reports presents a promising solution for enhancing the efficiency of the diagnostic process and reducing human error. However, existing methods require training on large datasets of image-report pairs, which are often scarce. Moreover, the accuracy of reports generated with limited paired data significantly diminishes. To address these challenges, this study introduces a data-efficient method that integrates the retrieval of similar reports with text fusion enhancements to tackle the scarcity of image-report pairs and generate accurate radiology reports. Our method is compared with several state-of-the-art approaches, showing advancements on the MIMIC-CXR and IU X-ray benchmarks with the same limited data pairs. It achieves near-optimal results on MIMIC-CXR and comparable results on IU-Xray, highlighting not only its effectiveness and potential to improve radiological diagnosis with fewer image reports but also its ability to generate more accurate reports. By enhancing cross-modal feature interaction and demonstrating higher diagnostic accuracy, this work contributes to the fields of clinical medicine and artificial intelligence. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Radiology

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The Digital Twin City in Enhancing Flood Evacuation Systems: Future Opportunities and Challenges

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IEEE Access 2025年 13卷 32371-32383页

作者： Setijadi Prihatmanto, Ary Prasetyadi, Abdurrakhman Yoganingrum, Ambar Sutriadi, Ridwan Hadiana, Ana Bandung Institute of Technology School of Electrical and Informatics Engineering Bandung40132 Indonesia National Research and Innovation Agency Research Center for Data and Information Science Bandung40231 Indonesia Bandung Institute of Technology School of Architecture Planning and Policy Development Bandung40132 Indonesia

In recent decades, the increasing frequency and severity of natural disasters have necessitated the development of more effective disaster management systems. This paper explores the potential of digital twin technology to enhance flood evacuation systems. A digital twin-city model can simulate urban environments, predict flood inundation, and identify optimal evacuation routes in real-time. The study conducts a systematic literature review to assess current advancements, evaluating the integration of 3D city models, artificial intelligence, and Internet of things sensors in flood evacuation planning. Results indicate that digital twin technology significantly improves predictive capabilities and evacuation efficiency, although data integration, model accuracy, and computational demands remain. Future research should focus on enhancing data integration, refining simulation models, improving predictive analytics, developing advanced visualization tools, and validating models across diverse environments to ensure reliability and effectiveness. © 2025 The Authors.

关键词： Digital elevation model

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Teachers’ Motivation to Engage with Students in a Computer science and Computational Thinking Challenge: Does Motivation Conform to a ‘One-Size-Fits-All’ Model? 17th

Teachers’ Motivation to Engage with Students in a Computer ...

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17th International Conference on informatics in Schools: Situation, Evolution, and Perspectives, ISSEP 2024

作者： Feklistova, Lidia Jevsikova, Tatjana Gaál, Bence Pluhár, Zsuzsa Institute of Computer Science University of Tartu Tartu Estonia Institute of Data Science and Digital Technologies Vilnius University Vilnius Lithuania Faculty of Informatics Eötvös Loránd University Budapest Hungary

ISBN: (纸本)9783031734731

The international Bebras challenge (BC) aims to raise the students’ interest in computer science (CS) and contribute to their problem-solving and computational thinking (CT) skills development. Through solving short conceptual tasks that are close to real-life context, students obtain the essential literacy needed to be successful in the modern world. Although the initiative has been running for about 20 years, teachers’ motivation to engage with students in this challenge remains underexplored. This study employs self-determination theory as a theoretical framework and the work tasks motivation scale for teachers as an instrument. data collected from 334 teachers across Estonia, Hungary, and Lithuania were analyzed. The results showed teachers’ motivation most significantly stemmed from intrinsic motivation and identified regulation. There were no significant differences in all types of motivation between male and female teachers or teachers with different years of teaching experience. However, teachers who were more experienced in engaging in the BC demonstrated higher levels of almost all types of motivation. Mature teachers were more externally motivated than their younger colleagues. Teachers who used the Bebras tasks in their lessons had a higher level of intrinsic motivation and identified regulation than those who did not use these tasks. Teachers teaching various subjects showed different levels of identified regulation. The study provides implications for teachers, school administration, and the BC organization. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Students

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Dynamic k-Anonymity for Electronic Health Records: A Topological Framework 19th

Dynamic k-Anonymity for Electronic Health Records: A Topolo...

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19th International Workshop on data Privacy Management, DPM 2024, 8th International Workshop on Cryptocurrencies and Blockchain Technology, CBT 2024 and 10th Workshop on the Security of Industrial Control Systems and of Cyber-Physical Systems, CyberICPS 2024 which were held in conjunction with the 29th European Symposium on Research in Computer Security, ESORICS 2024

作者： Swaminathan, Arjhun Akgün, Mete Medical Data Privacy and Privacy Preserving Machine Learning Department of Computer Science University of Tübingen Tübingen Germany Institute for Bioinformatics and Medical Informatics Tübingen Germany

ISBN: (纸本)9783031823480

With the rapid digitization of Electronic Health Records (EHRs), fast and adaptive data anonymization methods have become increasingly important. While tools from topological data analysis (TDA) have been proposed to anonymize static datasets—allowing the creation of multiple generalizations for different anonymization needs from a single computation—the application to dynamic datasets remains unexplored. To address this, our work adapts existing methodologies to the dynamic setting. We develop an improved version of weighted persistence barcodes that track higher-dimensional holes in data, allowing us to edit persistence information on the fly. Additionally, we introduce filtration trimming, a novel technique designed to update persistence data quickly with minimal computing effort when data is added. Our work represents a significant advancement in healthcare data privacy, offering a refined approach to protecting highly sensitive and evolving patient data through dynamic k-anonymity. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Electronic health record

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CosPGD: an efficient white-box adversarial attack for pixel-wise prediction tasks 24

CosPGD: an efficient white-box adversarial attack for pixel-...

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Proceedings of the 41st International Conference on Machine Learning

作者： Shashank Agnihotri Steffen Jung Margret Keuper Data and Web Science Group University of Mannheim Germany Max-Planck-Institute for Informatics Saarland Informatics Campus Germany and Data and Web Science Group University of Mannheim Germany Data and Web Science Group University of Mannheim Germany and Max-Planck-Institute for Informatics Saarland Informatics Campus Germany

While neural networks allow highly accurate predictions in many tasks, their lack of robustness towards even slight input perturbations often hampers their deployment. Adversarial attacks such as the seminal projected gradient descent (PGD) offer an effective means to evaluate a model's robustness and dedicated solutions have been proposed for attacks on semantic segmentation or optical flow estimation. While they attempt to increase the attack's efficiency, a further objective is to balance its effect, so that it acts on the entire image domain instead of isolated point-wise predictions. This often comes at the cost of optimization stability and thus efficiency. Here, we propose CosPGD, an attack that encourages more balanced errors over the entire image domain while increasing the attack's overall efficiency. To this end, CosPGD leverages a simple alignment score computed from any pixel-wise prediction and its target to scale the loss in a smooth and fully differentiable way. It leads to efficient evaluations of a model's robustness for semantic segmentation as well as regression models (such as optical flow, disparity estimation, or image restoration), and it allows it to outperform the previous SotA attack on semantic segmentation. We provide code for the CosPGD algorithm and example usage at https://***/shashankskagnihotri/cospgd.

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Leveraging LLM in Genetic Programming Hyper-heuristics for Dynamic Microservice Deployment 37th

Leveraging LLM in Genetic Programming Hyper-heuristics for...

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37th Australasian Joint Conference on Artificial Intelligence, AJCAI 2024

作者： Fang, Zhengxin Ma, Hui Chen, Gang Hartmann, Sven Wang, Chen Centre for Data Science and Artificial Intelligence and School of Engineering and Computer Science Victoria University of Wellington Wellington New Zealand Department of Informatics Clausthal University of Technology Clausthal-Zellerfeld Germany National Institute of Water and Atmospheric Research Wellington New Zealand

ISBN: (纸本)9789819603473

Microservice deployment in cloud computing is a challenging combinatorial optimization problem due to the complex dependencies among microservices and the intricate trade-offs among different QoS requirements, e.g., minimizing Energy Consumption (EC) vs. minimizing Communication Overhead (CO). Recently, some hyper-heuristics methods, particularly Genetic Programming Hyper-Heuristics (GPHH), have been proposed to automatically generate heuristics for solving dynamic microservice deployment problems. Meanwhile, Large Language Models (LLMs) are becoming popular for solving various domain-specific problems thanks to their strong ability to learn problem-related knowledge. However, hybridizing GPHH with LLM by combining their abilities in solving complex optimization problems remains unexplored. In this paper, we propose an LLM-enhanced Genetic Programming Hyper-Heuristic (LLM-GPHH) algorithm to evolve heuristics for the dynamic deployment of applications composed of microservices, to jointly optimize EC and CO. Our experiments on real-world datasets demonstrate the effectiveness of the newly proposed LLM-GPHH. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Genetic programming

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Estimating Citizen Personality Traits Using Social Media Posts 26th

Estimating Citizen Personality Traits Using Social Media Pos...

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26th International Conference on Asia-Pacific Digital Libraries, ICADL 2024

作者： Iwasaki, Taisei Seki, Yohei Kashino, Wakako Keyaki, Atsushi Kando, Noriko Graduate School of Comprehensive Human Sciences University of Tsukuba Tsukuba Japan Institute of Library Information and Media Science University of Tsukuba Tsukuba Japan National Institute for Japanese Language and Linguistics Tokyo Japan Faculty of Social Data Science Hitotsubashi University Tokyo Japan National Institute of Informatics Tokyo Japan The Graduate University for Advanced Studies Tokyo Japan

ISBN: (纸本)9789819608676

Personality traits are crucial factors that influence individual behavior and responses. In this study, we introduced both personality traits and experience traits in an analysis of the characteristics of citizens using social media posts from different cities. The personality traits referenced some facets of the "Big Five Model" of personality. The experience traits were introduced considering the close relationship between personality traits and behavior. Specifically, we labeled social media posts from each city with two labels, representing a personality trait and an experience trait. We then examined how these traits reflect the tendencies in citizens’ behavior. The personality traits defined in this study were "Altruism," "Artistic Interest," "Adventurousness," "Gregariousness," and "Activity." We assigned the labels manually and fine-tuned large language models to assign the personality trait labels to cities automatically. Finally, we analyzed the differences in personality trait trends between cities. Our experimental results showed that the F1 scores of the prediction models for both personality traits and experience traits exceeded 0.8. The analysis of social media posts using the trained models demonstrated that the citizens of a certain city had significantly higher scores for the personality traits "Artistic Interest" and "Gregariousness" than those from other cities, which was consistent with the results of previous questionnaire-based studies. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Behavioral Analysis City Modeling Large-scale Language Model Personality Traits Social Media

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A decision-making framework for user authentication using keystroke dynamics

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Computers and Security 2025年 155卷

作者： Medvedev, Viktor Budžys, Arnoldas Kurasova, Olga Vilnius University Faculty of Mathematics and Informatics Institute of Data Science and Digital Technologies Akademijos str. 4 Vilnius08412 Lithuania

Increasingly sophisticated cyber attacks threaten critical infrastructures, requiring more trusted user authentication mechanisms. In this work, we propose a deep learning-based user authentication framework that combines keystroke dynamics with Siamese neural networks to differentiate legitimate users from impostors. A key challenge in this area is the variability in password lengths, which leads to different feature sizes and complicates model training. Our approach uses interpolation-based data fusion strategies to standardize the number of keystroke features, ensuring consistency across different datasets and password lengths. Through experiments on the fused CMU and KeyRecs datasets, we have evaluated the effectiveness of the proposed decision-making framework with adaptive threshold strategies. The threshold strategy determines how the final decision boundary is set with respect to the user's baseline typing behavior. We empirically evaluated the framework on fused data, achieving an equal error rate as low as 0.11–0.12, indicating strong efficacy in detecting insider threats. We show how the obtained Siamese neural network with triplet loss function can be used to distinguish genuine users from impostors even under different input conditions, contributing to more robust and scalable intrusion detection systems. © 2025 Elsevier Ltd

关键词： Network intrusion

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Virtual staining for pathology: Challenges, limitations and perspectives

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Intelligent Oncology 2025年第2期1卷 105-119页

作者： Weiping Lin Yihuang Hu Runchen Zhu Baoshun Wang Liansheng Wang Department of Computer Science School of Informatics Xiamen University Xiamen Fujian 361005 China National Institute for Data Science in Health and Medicine Xiamen University Xiamen Fujian 361005 China

In pathological examinations, tissue must first be stained to meet specific diagnostic requirements, a meticulous process demanding significant time and expertise from specialists. With advancements in deep learning, this staining process can now be achieved through computational methods known as virtual staining. This technique replicates the visual effects of traditional histological staining in pathological imaging, enhancing efficiency and reducing costs. Extensive research in virtual staining for pathology has already demonstrated its effectiveness in generating clinically relevant stained images across a variety of diagnostic scenarios. Unlike previous reviews that broadly cover the clinical applications of virtual staining, this paper focuses on the technical methodologies, encompassing current models, datasets, and evaluation methods. It highlights the unique challenges of virtual staining compared to traditional image translation, discusses limitations in existing work, and explores future perspectives. Adopting a macro perspective, we avoid overly intricate technical details to make the content accessible to clinical experts. Additionally, we provide a brief introduction to the purpose of virtual staining from a medical standpoint, which may inspire algorithm-focused researchers. This paper aims to promote a deeper understanding of interdisciplinary knowledge between algorithm developers and clinicians, fostering the integration of technical solutions and medical expertise in the development of virtual staining models. This collaboration seeks to create more efficient, generalized, and versatile virtual staining models for a wide range of clinical applications.

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Frequency-Domain Refinement of Vision Transformers for Robust Medical Image Segmentation Under Degradation

Frequency-Domain Refinement of Vision Transformers for Robus...

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2025 IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2025

作者： Karimijarbigloo, Sanaz Kolahi, Sina Ghorbani Azad, Reza Bagci, Ulas Merhof, Dorit Tarbiat Modares University Department of Industrial and Systems Engineering Tehran Iran Northwestern University Department of Radiology Chicago United States University of Regensburg Faculty of Informatics and Data Science Regensburg Germany Fraunhofer Institute for Digital Medicine Mevis Bremen Germany

ISBN: (纸本)9798331510831

Medical image segmentation is crucial for precise diagnosis, treatment planning, and disease monitoring in clinical settings. While convolutional neural networks (CNNs) have achieved remarkable success, they struggle with modeling long-range dependencies. Vision Transformers (ViTs) address this limitation by leveraging self-attention mechanisms to capture global contextual information. However, ViTs often fall short in local feature description, which is crucial for precise segmentation. To address this issue, we reformulate self-attention in the frequency domain to enhance both local and global feature representation. Our approach, the Enhanced Wave Vision Transformer (EW-ViT), incorporates wavelet decomposition within the self-attention block to adaptively refine feature representation in low and high-frequency components. We also introduce the Prompt-Guided High-Frequency Refiner (PGHFR) module to handle image degradation, which mainly affects high-frequency components. This module uses implicit prompts to encode degradation-specific information and adjust high-frequency representations accordingly. Additionally, we apply a contrastive learning strategy to maintain feature consistency and ensure robustness against noise, leading to state-of-the-art (SOTA) performance in medical image segmentation, especially under various conditions of degradation. Source code is available at GitHub. © 2025 IEEE.

关键词： Convolutional neural networks

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