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

作者： Dong, Aimei Xu, Jingyuan Wang, Long Lv, Guohua Zhao, Guixin Cheng, Jinyong Qilu University of Technology Shandong Academy of Sciences Jinan China Faculty of Computer Science and Technology Qilu University of Technology Shandong Academy of Sciences China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (纸本)9798350368741

Multimodal image fusion aims to merge features from different modalities to create a comprehensively representative image. However, existing medical image fusion methods often struggle to handle noise generated during image acquisition, significantly diminishing their impact on visual quality. To address these challenges, we propose a semantically text-guided medical image fusion model, named TDMF. Specifically, TDMF guides classical image fusion through textual semantics and effectively coordinates the resolution of degradation and interaction issues during the fusion process. By integrating text encoders and interactive fusion modules, TDMF establishes a unified framework for denoising and interactive fusion of medical images. Extensive experiments have demonstrated that our proposed text-guided image fusion strategy offers significant advantages over state-of-the-art methods in medical image fusion performance. © 2025 IEEE.

关键词： denoising Medical image fusion text-guided fusion

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HiRes: Hierarchical Feature Optimization and Rescorer for Automatic ICD Coding

HiRes: Hierarchical Feature Optimization and Rescorer for Au...

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

作者： Liang, Zhenpeng Guan, Hongjiao Lu, Wenpeng Peng, Xueping Xu, Bing Yang, Muyun Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Qilu University of Technology Shandong Academy of Sciences Jinan China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China Australian Artificial Intelligence Institute University of Technology Sydney Sydney Australia Faculty of Computing Harbin Institute of Technology Harbin China

ISBN: (纸本)9798350368741

The International Classification of Diseases (ICD) coding assigns standardized codes to diseases. Automating this process enhances the efficiency and accuracy of clinical records processing. However, current methods struggle with noisy and lengthy clinical texts, making it difficult to ensure the reliability of feature extraction. Furthermore, they typically make separate binary predictions for each code, overlooking the dependencies between them. To address these issues, we propose a novel model called Hierarchical Feature Optimization and Rescorer (HiRes). We employ a cascaded convolution architecture to mitigate noise and enhance feature representation. Additionally, a masked autoencoder-based rescorer is introduced to capture ICD code interdependencies, refining initial predictions for improved accuracy. The model also considers the inconsistencies in code representations. Experiments on the MIMIC datasets demonstrate the effectiveness of our model. © 2025 IEEE.

关键词： automatic ICD coding cascaded convolutional network multi-label classification

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Mitigating Over-Unlearning in Machine Unlearning with Synthetic Data Augmentation 24th

Mitigating Over-Unlearning in Machine Unlearning with Synt...

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24th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Wang, Baohai Qu, Youyang Gao, Longxiang Li, Conggai Li, Lin Smith, David Jinan China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China Data61 Commonwealth Science and Industry Research Organization CanberraVIC Australia College of Business and Law Royal Melbourne Institute of Technology MelbourneVIC Australia

ISBN: (纸本)9789819615445

In machine learning, data privacy and security has become an increasingly growing concern. The introduction of machine unlearning offers the ability to address this issue through the removal of personal and sensitive data from trained models to comply with laws, regulations, and user privacy requirements. However, despite the significant benefits of this technique, performing unlearning operations is only sometimes smooth in practice. When we attempt to remove specific data, the model may over-adjust, resulting in its performance on unlearned data. This phenomenon not only reduces the accuracy of the model on known data but also affects its ability to generalize new data, thus weakening the model’s overall performance. Therefore, in this paper, we analyze the phenomenon of over-unlearning;firstly, we explore how to mitigate the effects of over-unlearning by generating synthetic data to fill in the forgotten parts, using data synthesis-based techniques. Secondly, we combine the data synthesis-based compensation strategy with model fine-tuning to further improve the model’s adaptability and generalization capability by further training the model to accommodate synthetic data. Through comprehensive experimentation, we verify the effectiveness of the proposed data synthesis-based compensation strategy. The experimental results show that the data synthesis-based compensation strategy can effectively mitigate the effects of the over-unlearning phenomenon, as well as maintaining the stability and accuracy of the model after removing specific data. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Data assimilation

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Pattern Matching of Positive and Negative DNA Sequences with General Gaps and One-off Constraints 10th

Pattern Matching of Positive and Negative DNA Sequences with...

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10th China Health Information Processing Conference, CHIP 2024

作者： Zhao, Long Zhen, Yicong Lu, Yue Guan, Hongjiao Dong, Xiangjun Jinan China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (纸本)9789819637546

The symmetrical DNA double-helix structure stores a large amount of complex and valuable biological information. Pattern matching of DNA sequence can count the location and number of gene fragments, which is beneficial to state tracking and targeted therapy of pathogenic genes. However, most sequence pattern matching methods target the entire sequence rather than frequent sequence patterns, which increase the computational complexity. In addition, current research methods ignore gene fragments with missing bases, and there is no unified analysis method for negative sequence pattern. In this case, the accuracy and efficiency of biological information analysis is significantly influenced. In view of the low efficiency and the small number of DNA sequence matching the paths obtained in the existing analysis methods, in this paper, we propose a method for pattern matching of positive and negative DNA sequences with general gaps and one-off constraints. First of all, the mined frequent sequence patterns are taken as patterns in the matching process, in order to make pattern matching more practical. Additionally, the negative sequences are used as pattern P for pattern matching, and reasonable gaps are inserted to make the matching process more flexible. Then, a detection mechanism for two duplicate elements is proposed, which not only effectively prunes the matching paths that do not meet the one-off constraint, but also accurately matches all information and eliminates redundancy. Experiments on the DNA sequences of 10 real species show that the algorithm can effectively deal with negative sequence patterns and obtain more matching paths, with the running efficiency to be improved. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Gene therapy

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Federated Meta Continual Learning for Efficient and Autonomous Edge Inference 24th

Federated Meta Continual Learning for Efficient and Autono...

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24th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Li, Bingze Ho, Stella Qu, Youyang Xu, Chenhao Luan, Tom H. Gao, Longxiang Jinan China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China Department of Biomedical Engineering The University of Melbourne VIC Melbourne Australia School of Information Technology Deakin University VIC Geelong Australia School of Cyber Engineering Xidian University Xi’an China

ISBN: (纸本)9789819615476

With an increasing volume of data being collected and processed on edge, Federated Learning (FL) has become a dominant approach to train shared global models on distributed data held by diverse edge clients without exposing privacy. However, with expectations of high-quality user experience, instantaneous intelligent decisions must be made at the network edge to meet performance, security, and latency requirements. Intuitively, this challenge becomes more daunting as edge devices are constrained by limited computing and storage resources. Furthermore, edge clients need to continuously acquire, update, accumulate, and apply knowledge in domains requiring the processing of sensitive data and long-term model training. It has been proven that edge clients often forget previous tasks knowledge after learning new tasks, resulting in catastrophic forgetting. To mitigate this, we propose a Federated Meta Continual Learning (FMCL) approach. It requires edge clients not only to learn new tasks quickly but also to memorize rapidly. Specifically, we adopted a MAML (Model-Agnostic Meta-Learning) framework with memory replay, setting up buffers at edge clients to consolidate old tasks. In such a way, the model learns optimal initialization to quickly adapt to all seen tasks while preventing catastrophic forgetting. Extensive evaluation results indicate that FMCL performs well in alleviating catastrophic forgetting and achieves a good balance between learning new tasks and remembering existing tasks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Federated learning

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A Hybrid-Feature-Based Autoencoder Model for Predicting Traffic Flow to Accelerate Ambulance Medical Response Time in internet of Vehicles

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IEEE internet of Things Journal 2025年第9期12卷 11397-11407页

作者： Li, Yongmeng Tian, Yu Liu, Wenjian Gao, Longxiang Tian, Hui Xing, Lumin The First Affiliated Hospital of Shandong First Medical University Shandong Provincial Qianfoshan Hospital Shandong Key Laboratory of Digital Diagnosis and Treatment of Thoracic Oncology Shandong Engineering Research Center of Intelligent Surgery Shandong Jinan250014 China Qilu Hospital of Shandong University Department of Thoracic Surgery Shandong Jinan250012 China City University of Macau Faculty of Data Science China Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan250014 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan250000 China The First Affiliated Hospital of Shandong First Medical University Shandong Provincial Qianfoshan Hospital Shandong Key Laboratory of Digital Diagnosis and Treatment of Thoracic Oncology Shandong Jinan250014 China

Traffic congestion during ambulance travel can delay medical response times. With the growing availability of traffic data, computational methods for traffic flow prediction are attracting significant attention. However, current computational prediction models have the following shortcomings. Fully connected networks require extensive feature engineering yet struggle to capture local traffic flow features. Their high parameter count increases training time and overfitting risk, while varying traffic conditions hinder model generalization. Therefore, in this work, we propose a hybrid-feature-based autoencoder (HFAE) model to predict traffic flow and accelerate ambulance medical response time in internet of Vehicles. Specifically, the HFAE model simultaneously considers local features from the convolutional neural network and global features from the fully connected neural network. Additionally, the HFAE model uses an autoencoder to reconstruct the original input, capturing the intrinsic structure of the traffic flow data. Specifically, the root mean-squared error and mean absolute error scores of the HFAE model are better than those of the three mainstream models. © 2014 IEEE.

关键词： Ambulances

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Towards Robust Video Anomaly Detection in 6G Networks: A Scene-Adaptive Framework

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IEEE Network 2025年

作者： Zhang, Sijia Yu, Yong Tao, Xiaoling Wang, Lianhai Shaanxi Normal University School of Computer Science China Guilin University of Electronic Technology Guangxi Cooperative Innovation Center of Cloud Computing and Big Data China Qilu University of Technology Shandong Academy of Sciences Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing China

With the rapid advancement of 5th Generation (5G) networks, their low latency and high bandwidth facilitate the development of smart city systems. Among the smart city systems, anomaly detection in surveillance videos has been an essential part due to its importance to public safety and security. With the transition to 6G technology, the practical applications of video anomaly detection are expanding to encompass a variety of scenarios, such as sidewalks, shopping malls and campuses, which exhibit significant differences. However, most existing anomaly detection methods deployed in the cloud typically rely on the fixed datasets for training and apply the trained uniform model to the edge services across different scenarios. The distinct environmental conditions may lead to varying performance of a uniform anomaly detection model. To alleviate these issues, we propose a scene-adaptive video anomaly detection framework in 6G-based applications. The proposed framework leverages the advantages of 6G networks to facilitate localized processing and rapid adaptation to diverse environments. To enable the anomaly detection model to adapt more robustly to local environments, the proposed framework integrates a pre-trained model and simulates anomalies to train the anomaly detection model in a denoising manner. By integrating pre-trained teacher models with edge computing nodes, the proposed method facilitates accurate anomaly detection while maintaining robust performance across varying scenarios. The framework utilizes both original data and simulated anomalous data to fine-tune the edge model, ensuring it aligns with the unique characteristics of each environment. Experimental results demonstrate the effectiveness of the proposed method across different scenarios, enabling smarter and more responsive surveillance systems in the 6G era. © 2025 IEEE.

关键词： Video analysis

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Adaptive ELM-Based Time-Constrained Control of Uncertain Robotic Manipulators With Minimum Learning Computation

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IEEE Transactions on Emerging Topics in computing 2025年

作者： Jin, Xiaozheng Hou, Yuhan Wu, Xiaoming Chi, Jing Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Shandong Jinan250353 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Jinan250013 China Qilu Institute of Technology School of Intelligent Manufacturing and Control Engineering Shandong Jinan250200 China Shandong University of Finance and Economics School of Computing and Artificial Intelligence Shandong Jinan250014 China

This paper studies the extreme learning machine (ELM)-based time-constrained trajectory tracking control of a class of robotic manipulator systems with model/input uncertainties, matched and mismatched disturbances. A uniform robust exact differentiator (URED) is developed to construct a multi-layer virtual control framework of the robotic manipulator system. Under the control framework, adaptive ELM-based control strategies are proposed to suppress the influence of the uncertainties and disturbances layer-by-layer, combining with the minimum learning parameter (MLP) technique which is employed to reduce the calculation complexity of ELM parameters. The significance of this study is that general model/input uncertainties, matched and mismatched disturbances can be effectively compensated within a limited time, so that bounded trajectory tracking of the robotic manipulator can be achieved avoiding the complexity explosion issue. The time-constrained stability of the closed-loop robotic error systems is proved through Lyapunov stability theory. Finally, comparative simulations are employed to display the feasibility and superiority of the designed robust adaptive ELM-based control schemes of a robotic manipulator system. © 2013 IEEE.

关键词： Robust control

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A detection model for dust deposition on photovoltaic (PV) panels based on light transmittance estimation

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Energy 2025年 322卷

作者： Chen, Linhong Fan, Siyuan Sun, Shengyao Cao, Shengxian Sun, Tianyi Liu, Peng Gao, Han Zhang, Yanhui Ding, Wei School of Automation Engineering Northeast Electric Power University Jilin 132012 China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Jinan250014 China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan250014 China

Dust deposition on photovoltaic (PV) panels significantly reduces light transmittance and power conversion efficiency. Therefore, real-time dust detection systems are crucial for proactive cleaning and maintenance to improve light absorption and the operational efficiency of PV systems. This paper developed an end-to-end PV dust detection model, DVNET, based on light transmittance estimation. The model quantifies the dust density on PV panels using image processing to estimate light transmittance and determine optimal cleaning strategies. The DVNET architecture captures the mapping relationship between light transmittance and dust density. The model calculates the light transmittance of dusty images, enabling a quantitative assessment of dust deposition. Images are acquired and preprocessed to eliminate noise and irrelevant information and obtain optimal model training conditions. Experiments are conducted to evaluate model performance. The results indicate that the proposed model accurately estimates light transmittance using images of PV panels and generates transmittance maps to visualize the dust distribution on the panel surfaces. DVNET outperforms five benchmark models, achieving the lowest mean square error (MSE) of 0.00044. The performances of the squeeze-and-excitation block (SEBlock) module, the convolutional block attention module (CBAM), and the multiply-add-combine (MAC) attention mechanism are evaluated. The experimentally determined relative error (RE) is below 0.03 for uniform and non-uniform transmittance. The observed and predicted correlations between dust deposition and transmittance are highly similar for PV panels with different dust densities, demonstrating the model's applicability in real-world scenarios. The experiment demonstrated that the DVNET model exhibits low computational resource consumption, fast training time, as well as remarkable scalability and computational performance on large-scale datasets. © 2025 Elsevier Ltd

关键词： Dust deposition density Image processing PV panels Transmittance estimation

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Comprehensive Feature Processing Based on Attention Mechanism for Co-Salient Object Detection

Comprehensive Feature Processing Based on Attention Mechanis...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Guohua Lv Mao Yuan Zengbin Zhang Zhengyang Zhang Zhenhui Ding Guangxiao Ma Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China Shandong Key Laboratory of Ubiquitous Intelligent Computing Jinan China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Co-salient object detection (CoSOD) aims to detect common salient objects across multiple related images. However, existing methods often struggle with limited attention coverage, missing some co-salient objects. To address this, we propose a two-stage feature processing module (FPM) comprising comprehensive feature extraction module (CFE) and feature enhancement module (FEM). CFE extracts comprehensive cosalient features while reducing background noise, and FEM enhances feature representation and adjusts attention weights for full object coverage. Additionally, we introduce an adversarial learning module (ALM) to improve prediction quality by reducing noise in the co-salient regions. Extensive experiments on three benchmark datasets—CoCA, CoSOD3k, and CoSal2015—demonstrate that our model significantly outperforms state-of-the-art methods. The source code is available at https://***/yaobaimiao/CFPAM.

关键词： Attention mechanisms Source coding Noise Object detection Benchmark testing Feature extraction Adversarial machine learning Finite element analysis Background noise Speech processing

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