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IEEE International Conference on Unmanned Systems (ICUS)

作者： Linlu Gao Mingrui Yin Fuyuan Xiao Zehong Cao The School of Big Data and Software Engineering Chongqing University Chongqing China College of Computer Science and Technology Zhejiang University Hangzhou China STEM Mawson Lakes Campus University of South Australia Adelaide Australia

Effective target detection and recognition are essential in remote sensing image field, and complex evidence theory (CET) is widely used for this purpose. However, measuring conflict between complex basic belief assignments (CBBAs) in CET is challenging. This study proposes a complex belief Jensen-Shannon divergence based on the complex Pignistic transformation to measure conflict, accounting for quantum interference effects in CBBAs. We analyze the properties of the CBJS divergence, including boundedness, nondegeneracy, and symmetry. Numerical examples are presented to show the effectiveness of the conflict measure method and the potential of improving the robustness of intelligent interpretation system. Moreover, an algorithm for decision-making is presented and applied in pattern recognition illustrating its superiority.

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Overcoming Heterogeneous data in Federated Medical Vision-Language Pre-training: A Triple-Embedding Model Selector Approach 39

Overcoming Heterogeneous Data in Federated Medical Vision-La...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Wang, Aowen Zhang, Zhiwang Wang, Dongang Wang, Fanyi Hu, Haotian Guo, Jinyang Zhou, Yipeng Pang, Chaoyi Wen, Shiting College of Computer Science and Technology Zhejiang University China School of Computing and Data Engineering NingboTech University China Brain and Mind Centre The University of Sydney Australia Zhejiang Leapmotor Technology Co. Ltd China Institute of Artificial Intelligence Beihang University China School of Computing Macquarie University Australia

ISBN: (纸本)157735897X

The scarcity of data in the medical field brings challenges to collaborative training in medical vision-language pre-training (VLP) across different clients Thus, collaborative training in medical VLP faces two significant challenges: First, the medical data requires privacy and therefore cannot be directly shared across different clients. Second, medical data distribution across institutes is typically heterogeneous, hindering local model alignment and representation capabilities. To simultaneously overcome these two challenges, we propose a framework called personalized model selector with fused multimodal information (PMS-FM). The contribution of PMS-FM is two-fold: 1) PMS-FM uses embeddings to represent information in different formats, allowing for the fusion of multimodal data. 2) PMS-FM adapts to personalized data distributions by training multiple models. A model selector then identifies and selects the best-performing model for each individual client. Extensive experiments with multiple real-world medical datasets demonstrate the superb performance of PMS-FM over existing federated learning methods on different zero-shot classification tasks. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Federated learning

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Reward-agnostic Fine-tuning: Provable Statistical Benefits of Hybrid Reinforcement Learning

arXiv

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arXiv 2023年

作者： Li, Gen Zhan, Wenhao Lee, Jason D. Chi, Yuejie Chen, Yuxin Department of Statistics and Data Science Wharton School University of Pennsylvania PhiladelphiaPA19104 United States Department of Electrical and Computer Engineering Princeton University PrincetonNJ08544 United States Department of Electrical and Computer Engineering Carnegie Mellon University PittsburghPA15213 United States

This paper studies tabular reinforcement learning (RL) in the hybrid setting, which assumes access to both an offline dataset and online interactions with the unknown environment. A central question boils down to how to efficiently utilize online data collection to strengthen and complement the offline dataset and enable effective policy fine-tuning. Leveraging recent advances in reward-agnostic exploration and model-based offline RL, we design a three-stage hybrid RL algorithm that beats the best of both worlds — pure offline RL and pure online RL — in terms of sample complexities. The proposed algorithm does not require any reward information during data collection. Our theory is developed based on a new notion called single-policy partial concentrability, which captures the trade-off between distribution mismatch and miscoverage and guides the interplay between offline and online data. © 2023, CC0.

关键词： Reinforcement learning

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Confounder-Aware Medical data Selection for Fine-Tuning Pretrained Vision Models

Confounder-Aware Medical Data Selection for Fine-Tuning Pret...

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IEEE International Symposium on Biomedical Imaging

作者： Anyang Ji Qingbo Kang Wei Xu Changfan Wang Kang Li Qicheng Lao West China Biomedical Big Data Center West China Hospital Sichuan University Chengdu China College of Computer Science Sichuan University Chengdu China Shanghai Artificial Intelligence Laboratory Shanghai China School of Biomedical Engineering University of Science and Technology of China Hefei China School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

The emergence of large-scale pre-trained vision foundation models has greatly advanced the medical imaging field through the pre-training and fine-tuning paradigm. However, selecting appropriate medical data for downstream fine-tuning remains a significant challenge considering its annotation cost, privacy concerns, and the detrimental effects of confounding variables. In this work, we present a confounder-aware medical data selection approach for medical dataset curation aiming to select minimal representative data by strategically mitigating the undesirable impact of confounding variables while preserving the natural distribution of the dataset. Our approach first identifies confounding variables within data and then develops a distance-based data selection strategy for confounder-aware sampling with a constrained budget in the data size. We validate the superiority of our approach through extensive experiments across diverse medical imaging modalities, highlighting its effectiveness in addressing the substantial impact of confounding variables and enhancing the fine-tuning efficiency in the medical imaging domain, compared to other data selection approaches.

关键词： Visualization data privacy Costs Foundation models Annotations Clustering algorithms Manuals data models Calibration Biomedical imaging

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Graph Neural Networks for Wireless Networks: Graph Representation, Architecture and Evaluation

arXiv

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arXiv 2024年

作者： Lu, Yang Li, Yuhang Zhang, Ruichen Chen, Wei Ai, Bo Niyato, Dusit School of Computer and Technology China Collaborative Innovation Center of Railway Traffic Safety Beijing Jiaotong University Beijing100044 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore School of Electronic and Information Engineering Beijing Jiaotong University Beijing100044 China

Graph neural networks (GNNs) have been regarded as the basic model to facilitate deep learning (DL) to revolutionize resource allocation in wireless networks. GNN-based models are shown to be able to learn the structural information about graphs representing the wireless networks to adapt to the time-varying channel state information and dynamics of network topology. This article aims to provide a comprehensive overview of applying GNNs to optimize wireless networks via answering three fundamental questions, i.e., how to input the system parameters of wireless networks into GNNs, how to improve the expressive performance of GNNs, and how to evaluate GNNs. Particularly, two graph representations are given to transform wireless network parameters into graph-structured data. Then, we focus on the architecture design of the GNN-based models via introducing the basic message passing as well as model improvement methods including multi-head attention mechanism and residual structure. At last, we give task-oriented evaluation metrics for DL-enabled wireless resource allocation schemes. We also highlight certain challenges and potential research directions for the application of GNNs in wireless networks. © 2024, CC BY.

关键词： Graph neural networks

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Classification of cow diet based on milk Mid Infrared Spectra: A data analysis competition at the "International Workshop on Spectroscopy and Chemometrics 2022"

Classification of cow diet based on milk Mid Infrared Spectr...

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作者： Frizzarin, Maria Visentin, Giulio Ferragina, Alessandro Hayes, Elena Bevilacqua, Antonio Dhariyal, Bhaskar Domijan, Katarina Khan, Hussain Ifrim, Georgiana Nguyen, Thach Le Meagher, Joe Menchetti, Laura Singh, Ashish Whoriskey, Suzy Williamson, Robert Zappaterra, Martina Casa, Alessandro Teagasc Animal & Grassland Research and Innovation Centre Moorepark Ireland School of Mathematics and Statistics University College Dublin Ireland Department of Veterinary Medical Sciences University of Bologna Italy Teagasc Food Research Centre Ashtown Ireland Teagasc Food Research Centre Moorepark Ireland School of Computer Science University College Dublin Ireland Department of Mathematics and Statistics National University of Ireland Maynooth Ireland Insight Centre for Data Analytics University College Dublin Ireland School of Biosciences and Veterinary Medicine University of Camerino Italy School of Electronics Electrical Engineering and Computer Science Queen's University Belfast United Kingdom Department of Agricultural and Food Sciences University of Bologna Italy Faculty of Economics and Management Free University of Bozen-Bolzano Italy

In April 2022, the Vistamilk SFI Research Centre organized the second edition of the "International Workshop on Spectroscopy and Chemometrics – Applications in Food and Agriculture". Within this event, a data challenge was organized among participants of the workshop. Such data competition aimed at developing a prediction model to discriminate dairy cows’ diet based on milk spectral information collected in the mid-infrared region. In fact, the development of an accurate and reliable discriminant model for dairy cows’ diet can provide important authentication tools for dairy processors to guarantee product origin for dairy food manufacturers from grass-fed animals. Different statistical and machine learning modelling approaches have been employed during the workshop, with different pre-processing steps involved and different degree of complexity. The present paper aims to describe the statistical methods adopted by participants to develop such classification model. © 2023

关键词： Machine learning

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Predicting Hand Injury Severity Using Bayesian Networks in Cloud-Based Emergency Medicine

Predicting Hand Injury Severity Using Bayesian Networks in C...

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International Conference on Communications and Signal Processing

作者： S Yuvaraj K. Thinakaran S Porandla Srinivas S. Sivakumar M.V Ishwarya P. Thirumaraiselvan Department of Electronics and Communication Engineering Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai Tamil Nadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of Information Technology Malla Reddy Institute of Engineering and Technology Hyderabad Telangana India Department of Computer Science Thanthai Hans Roever College (Autonomous) Perambalur Tamil Nadu India Artificial Intelligence and Data science Department Agni College of Technology Thazhambur Tamil Nadu India Department of Electronics and Communication Engineering Adhiparasakthi Engineering College Melmaruvathur Tamil Nadu India

ISBN: (数字)9798350353068

ISBN: (纸本)9798350353075

Predicting the severity of hand injuries in emergency care is the focus of this study, which suggests using cloud-based Bayesian networks. It is crucial to have accurate severity prediction in order to treat emergency hand injuries promptly since they are prevalent. By using the scalability and flexibility of cloud computing, this method constructs a Bayesian severity assessment network with patient data, injury characteristics, and historical medical records. The model’s training data comes from a massive database of hand injury cases with different results. Emergency medical professionals may use real-time parameter modifications to strengthen and adapt the Bayesian network. Through the use of the cloud, healthcare providers may simply exchange data and reach consensus. It demonstrates that, compared to other well-established methods of evaluating hand injury severity, the model performs better in making predictions. The suggested method improves severity forecasts and advances cloud-based emergency care. The method for emergency hand injury care uses Bayesian networks in the cloud to improve scalability and accessibility. The findings show that the data-driven and efficient hand injury severity prediction method might transform emergency care by improving patient outcomes.

关键词： Cloud computing Adaptation models Scalability Training data Medical services Transforms Predictive models

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Does Graph Distillation See Like Vision dataset Counterpart?

arXiv

引用

arXiv 2023年

作者： Yang, Beining Wang, Kai Sun, Qingyun Ji, Cheng Fu, Xingcheng Tang, Hao You, Yang Li, Jianxin School of Computer Science and Engineering Beihang University China Advanced Innovation Center for Big Data and Brain Computing Beihang University China National University of Singapore Singapore Carnegie Mellon University United States

Training on large-scale graphs has achieved remarkable results in graph representation learning, but its cost and storage have attracted increasing concerns. Existing graph condensation methods primarily focus on optimizing the feature matrices of condensed graphs while overlooking the impact of the structure information from the original graphs. To investigate the impact of the structure information, we conduct analysis from the spectral domain and empirically identify substantial Laplacian Energy Distribution (LED) shifts in previous works. Such shifts lead to poor performance in cross-architecture generalization and specific tasks, including anomaly detection and link prediction. In this paper, we propose a novel Structure-broadcasting Graph dataset Distillation (SGDD) scheme for broadcasting the original structure information to the generation of the synthetic one, which explicitly prevents overlooking the original structure information. Theoretically, the synthetic graphs by SGDD are expected to have smaller LED shifts than previous works, leading to superior performance in both cross-architecture settings and specific tasks. We validate the proposed SGDD across 9 datasets and achieve state-of-the-art results on all of them: for example, on the YelpChi dataset, our approach maintains 98.6% test accuracy of training on the original graph dataset with 1,000 times saving on the scale of the graph. Moreover, we empirically evaluate there exist 17.6% ∼ 31.4% reductions in LED shift crossing 9 datasets. Extensive experiments and analysis verify the effectiveness and necessity of the proposed designs. The code is available in the https://***/RingBDStack/SGDD. Copyright © 2023, The Authors. All rights reserved.

关键词： Distillation

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An Exploratory Investigation of Log Anomalies in Unmanned Aerial Vehicles

An Exploratory Investigation of Log Anomalies in Unmanned Ae...

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International Conference on Software engineering (ICSE)

作者： Dinghua Wang Shuqing Li Guanping Xiao Yepang Liu Yulei Sui Pinjia He Michael R. Lyu University of Technology Sydney Australia Southern University of Science and Technology China The Chinese University of Hong Kong China Nanjing University of Aeronautics and Astronautics China Department of Computer Science and Engineering Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology China The University of New South Wales Australia School of Data Science The Chinese University of Hong Kong Shenzhen (CUHK-Shenzhen) China Shenzhen Research Institute of Big Data China

ISBN: (数字)9798400702174

ISBN: (纸本)9798350382143

Unmanned aerial vehicles (UAVs) are becoming increasingly ubiqui-tous in our daily lives. However, like many other complex systems, UAVs are susceptible to software bugs that can lead to abnormal system behaviors and undesirable consequences. It is crucial to study such software bug-induced UAV anomalies, which are often mani-fested in flight logs, to help assure the quality and safety of UAV systems. However, there has been limited research on investigating the code-level patterns of software bug-induced UAV anomalies. This impedes the development of effective tools for diagnosing and localizing bugs within UAV system code. To bridge the research gap and deepen our understanding of UAV anomalies, we carried out an empirical study on this subject. We first collected 178 real-world abnormal logs induced by soft-ware bugs in two popular open-source UAV platforms, i.e., PX4 and Ardupilot. We then examined each of these abnormal logs and com-piled their common patterns. In particular, we investigated the most severe anomalies that led to UAV crashes, and identified their features. Based on our empirical findings, we further summarized the challenges of localizing bugs in system code by analyzing anoma-lous UAV flight data, which can offer insights for future research in this field.

关键词： Bridges Codes computer bugs Autonomous aerial vehicles Software Safety Complex systems

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Multi-task Learning for Multi-turn Dialogue Generation with Topic Drift Modeling 12

Multi-task Learning for Multi-turn Dialogue Generation with ...

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12th IEEE International Conference on Big Knowledge, ICBK 2021

作者： Zeng, Hongwei Hong, Zhenjie Liu, Jun Wei, Bifan Xi'An Jiaotong University School of Computer Science and Technology Xi'an China Xi'An Jiaotong University National Engineering Lab For Big Data Analytics Xi'an China Xi'An Jiaotong University School of Continuing Education Xi'an China

ISBN: (纸本)9781665438582

Multi-turn dialogue generation aims to generate natural and fluent responses that should be consistent with multiple consecutive utterances history. It is a more challenging task compared to its single-turn counterpart since it requires the model to capture the topic drift along with the multi-turn dialogue history. In this paper, we propose a multi-turn dialogue generation model which incorporates topic drift aware information into a hierarchical encoder-decoder framework to generate coherent responses. This model first utilizes a Convolutional Neural Network (CNN) based topic model to obtain the topic representation of each utterance. Then a topic drift model is employed to encode the sequential topics of multi-turn dialogue history to infer the topic of response. During the response generation, a specially designed topic drift aware generator is proposed to dynamically balance the impact of the inferred topic of response and local word structure. Fur-thermore, we employ multi-task learning to optimize the topic drift model and dialogue generation simultaneously. Extensive experimental results on two benchmark datasets (i.e. Cornell Movie Dialog Corpus and Ubuntu Dialogue dataset) indicate that our proposed model can generate more coherent responses, and significantly outperform other dialogue generation models. © 2021 IEEE.

关键词： Learning systems

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