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

作者： Zhenpeng Liang Hongjiao Guan Wenpeng Lu Xueping Peng Bing Xu Muyun Yang 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: (数字)9798350368741

ISBN: (纸本)9798350368758

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.

关键词： Codes Accuracy Computational modeling Noise Refining Encoding Reliability Speech processing Optimization Diseases

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An evolutionary multiobjective method based on dominance and decomposition for feature selection in classification

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Science China(information Sciences) 2024年第2期67卷 5-19页

作者： Jing LIANG Yuyang ZHANG Ke CHEN Boyang QU Kunjie YU Caitong YUE Ponnuthurai Nagaratnam SUGANTHAN School of Electrical and Information Engineering Zhengzhou University State Key Laboratory of Intelligent Agricultural Power Equipment School of Electrical Engineering and Automation Henan Institute of Technology School of Electronics and Information Zhongyuan University of Technology KINDI Center for Computing Research College of EngineeringQatar University

Feature selection in classification can be considered a multiobjective problem with the objectives of increasing classification accuracy and decreasing the size of the selected feature subset. Dominance-based and decomposition-based multiobjective evolutionary algorithms(MOEAs) have been extensively used to address the feature selection problem due to their strong global search capability. However, most of them face the problem of not effectively balancing convergence and diversity during the evolutionary process. In addressing the aforementioned issue, this study proposes a unified evolutionary framework that combines two search forms of dominance and decomposition. The advantages of the two search methods assist one another in escaping the local optimum and inclining toward a balance of convergence and diversity. Specifically, an improved environmental selection strategy based on the distributions of individuals in the objective space is presented to avoid duplicate feature subsets. Furthermore, a novel knowledge transfer mechanism that considers evolutionary characteristics is developed, allowing for the effective implementation of positive knowledge transfer between dominance-based and decomposition-based feature selection methods. The experimental results demonstrate that the proposed algorithm can evolve feature subsets with good convergence and diversity in a shorter time compared with 9 state-of-the-art feature selection methods on 20 classification problems.

关键词： evolutionary algorithms feature selection multiobjective optimization knowledge transfer classification

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A Lightweight Node Verification and Protection Strategy for Edge computing

A Lightweight Node Verification and Protection Strategy for ...

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Artificial Intelligence, networking and information Technology (AINIT), International Seminar on

作者： Jiayuan Du Guowei Zhang School of Cyber Science and Engineering Qufu Normal University Qufu China Key Laboratory of Computing Power Network and Information Security Ministry of Education Qilu University of Technology (Shandong Academy of Sciences) Jinan China

ISBN: (数字)9798350385557

ISBN: (纸本)9798350385564

To alleviate the burden on the core network, edge computing has rapidly advanced to support distributed computing. However, as sensitive information is delegated to the edge layer closer to endpoints, the privacy protection challenges faced by edge nodes intensify while striving to deliver high-quality services. Existing studies predominantly rely on various encryption algorithms to safeguard privacy information, such as identity, location, and offloading routes privacy. Unfortunately, these approaches exhibit high overhead and often neglect the potential maliciousness of the nodes themselves. To address these issues, this paper introduces a lightweight edge node verification and protection strategy. This strategy not only verifies the reliability of edge nodes but also enables anonymized uploading of private information. Numerical simulation results demonstrate the superiority of this strategy over existing schemes, showcasing reduced computational overhead while achieving enhanced protection.

关键词： Seminars Privacy Numerical simulation Mathematical models Performance analysis Security Reliability

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An Early Detection Model of Prostate Cancer Based on MRI 9

An Early Detection Model of Prostate Cancer Based on MRI

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9th IEEE International Conference on Data Science in Cyberspace, DSC 2024

作者： Liu, Yuankun Xing, Lumin Wang, Di Nie, Xin Zhang, Xueyan Wu, Di Liu, Wenjian The First Affiliated Hospital of Shandong First Medical University Shandong Provincial Qianfoshan Hospital Jinan 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 Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China Chongqing Jianzhu College Chongqing China Shandong Youth University of Political Science School of Information Work Office Jinan China University of Southern Queensland School of Mathematics Physics and Computing QLD Australia

ISBN: (纸本)9798350391367

Prostate cancer is a prevalent form of cancer among men, with a high rate of occurrence but relatively lower mortality compared to other types of cancer. Early detection is crucial because it allows for earlier intervention, potentially preventing progression to more severe stages, and improving patient outcomes. However, human diagnosis is time-consuming and inefficient, an autonomous early detection system is essential for saving lives. To address this challenge, we propose a novel MoE-MRIViT framework that uses advanced machine learning and computer vision techniques to analyze MRI images and identify features indicative of prostate cancer. The system processes the data using a vision transformer to extract precise prostate cancer features from MRI scans, and uses attention mechanisms to focus on significant areas. Through the MoE-MRIViT intervention in two stages, we have achieved superior results compared to the state-of-the-art (SOTA) methods. Experimental results demonstrate that our model achieves an impressive accuracy of 94.84%, outperforming the ResNet-101 and the basic ViT models, which achieve accuracies of 89.4% and 92.31%, respectively. © 2024 IEEE.

关键词： Urology

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Symbiosis Rather Than Aggregation: Towards Generalized Federated Learning via Model Symbiosis

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IEEE Internet of Things Journal 2025年

作者： Liu, Yuange Liu, Yuru Zhang, Weishan Zheng, Chaoqun Jiang, Wei Luo, Daobin Qiao, Qiao Meng, Lingzhao Zhao, Hongwei Yang, Su College of Computer Science and Technology Qingdao266580 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 National Supercomputer Center in Jinan Jinan250014 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Jinan250014 China Information Science Academy of China Electronics Technology Group Corporation Beijing100036 China Fudan University School of Computer Science Shanghai200433 China

Federated Learning (FL) faces significant challenges in scenarios with non-IID (non-independent and identically distributed) data distributions across participating clients. Traditional aggregation-based approaches often struggle with the inherent misalignment between local and global optimization objectives, leadingwhich leads to gradient divergence and suboptimal generalization performance. This paper proposes a novel FL framework that replaces conventional aggregation with a biologically inspired model symbiosis approach called FedSym, which employs a dual-level symbiotic mechanism. Ectosymbiosis performs coarse-grained hierarchical parameter recombinations through random layer-wise model combination, while endosymbiosis enables fine-grained intra-layer parameter fusion through weighted averaging, collectively steering model updates towards flatter loss landscapes. Our theoretical analysis demonstrates that FedSym's convergence rate is O(1T) under non-IID conditions, which matches the convergence properties of FedAvg. Extensive experimentsevaluations across multiple datasets and model architectures show that FedSym achieves substantial improvements over state-of-the-art FL methods, particularly in challenging scenarios with high data heterogeneity, and demonstrates robust performance across varying numbers of participating clients and federation scales. © 2014 IEEE.

关键词： Federated learning

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Spatial-Spectral Transformer for Local and Global Hyperspectral Image Classification

Spatial-Spectral Transformer for Local and Global Hyperspect...

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Signal, information and Data Processing (ICSIDP), IEEE International Conference on

作者： MengXin Cao Guixin Zhao Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

Deep learning techniques have become increasingly prevalent in hyperspectral image (HSI) classification. However, convolutional neural network (CNN)-based approaches often struggle with inefficient utilization of spectral sequence data and inadequate processing of fine-grained spectral details. In contrast, transformer models are inherently designed to capture sequence dependencies across spectral bands. Yet, most existing approaches tend to focus on either local or global feature extraction separately, which limits their ability to fully exploit the spectral information. To overcome these limitations, we propose the Local and Global Fusion Spectral Transformer (LGFST), an innovative architecture that combines Swin Transformer blocks, standard Transformer blocks, and a novel Local-Global Fusion (LGF) module. This architecture is designed to simultaneously capture both local and global features, thereby improving the extraction of detailed spectral information. The Swin Transformer block, integrated with patch merging and hierarchical attention mechanisms, expands the receptive field and enhances local feature representation through both intra- and cross-window interactions. Meanwhile, the Transformer block performs self-attention across all tokens to extract global features. The fusion of local and global features from these two blocks effectively improves the texture and discriminative power of spectral data, leading to enhanced classification performance, especially in scenarios with dense class distributions. Finally, the LGF module facilitates interaction between local and global feature sets, ensuring a seamless integration of information from both blocks. Extensive experiments on three benchmark HSI datasets demonstrate that LGFST outperforms conventional transformer models and sets new benchmarks in performance compared to state-of-the-art methods.

关键词： Merging Benchmark testing Transformers Feature extraction Distortion Sensors Data mining Standards Hyperspectral imaging Image classification

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Towards an Ontology of Traceable Impact Management in the Food Supply Chain

arXiv

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arXiv 2025年

作者： Gajderowicz, Bart Fox, Mark S. Gao, Yongchao Urban Data Centre School of Cities University of Toronto Toronto Canada Key Laboratory of Computing Power Network and Information Security Shandong Computer Science Center Qilu University of Technology Shandong Academy of Sciences Jinan China

The pursuit of quality improvements and accountability in the food supply chains, especially how they relate to food-related outcomes, such as hunger, has become increasingly vital, necessitating a comprehensive approach that encompasses product quality and its impact on various stakeholders and their communities. Such an approach offers numerous benefits in increasing product quality and eliminating superfluous measurements while appraising and alleviating the broader societal and environmental repercussions. A traceable impact management model (TIMM) provides an impact structure and a reporting mechanism that identifies each stakeholder’s role in the total impact of food production and consumption stages. The model aims to increase traceability’s utility in understanding the impact of changes on communities affected by food production and consumption, aligning with current and future government requirements, and addressing the needs of communities and consumers. This holistic approach is further supported by an ontological model that forms the logical foundation and a unified terminology. By proposing a holistic and integrated solution across multiple stakeholders, the model emphasizes quality and the extensive impact of championing accountability, sustainability, and responsible practices with global traceability. With these combined efforts, the food supply chain moves toward a global tracking and tracing process that not only ensures product quality but also addresses its impact on a broader scale, fostering accountability, sustainability, and responsible food production and consumption. © 2025, CC BY.

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Research on optimal scheduling decision method of mobile emergency power supply vehicle under emergency condition

Research on optimal scheduling decision method of mobile eme...

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IEEE Conference on Energy Internet and Energy System Integration (EI2)

作者： Zehao Zhang Jie Feng Cheng Guan Zeyu Liu Yiran Yan Xiyuan Xu State Grid Key Laboratory of Information & Network Security China Electric Power Research Institute Co. Ltd Beijing China

ISBN: (数字)9798331523527

ISBN: (纸本)9798331523534

In the current power grid emergency repair process, there is often a situation in which the mobile emergency power supply vehicles of different power supply units in a region "fight for themselves", resulting in the imbalance and insufficiency of emergency relief resources, which not only wastes valuable emergency resources, but also fails to guarantee the emergency power supply needs of the society. Based on this research, a global mobile emergency power supply vehicle allocation decision model is established to rationally allocate and dispatch limited and precious mobile emergency power supply vehicle resources in the event of power failure caused by disaster weather, and effectively improve the power supply recovery and emergency support ability of distribution network.

关键词： Emergency power supplies Energy Internet Optimal scheduling System integration Distribution networks Maintenance engineering power grids power system reliability Resource management Meteorology

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A Dirty Data Identification Method in Mechanical Equipment Condition Monitoring

A Dirty Data Identification Method in Mechanical Equipment C...

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Smart World Congress (SWC), IEEE

作者： Huijuan Hao Hongge Zhao Sijian Zhu Yue Feng 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 Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China Jinan China

ISBN: (数字)9798331520861

ISBN: (纸本)9798331520878

The rise of the Industrial Internet of Things (IIOT) has brought new opportunities for the mechanical equipment condition monitoring. The trend towards intelligent and automated production of mechanical equipment has become mainstream. However, in the actual operational environment, mechanical equipment is often mixed with a large amount of dirty data unrelated to equipment failures, which seriously affects equipment fault diagnosis and wastes maintenance resources. To improve the reliability of mechanical equipment health status analysis, this paper proposes a new method for dirty data identification suitable for mechanical equipment condition monitoring. First, utilizing the time-space position encoding (TSPE) to map positional relationships between sequences. Next, designing a time-dimension attention (TDA) mechanism to capture the correlation between multiple temporal attribute features and the temporal dependencies between preceding and subsequent data. Then, building a dirty data identification model using an improved Transformer network and Generative Adversarial network (GAN). Finally, employing an adaptive thresholding method to dynamically set thresholds, determining the degree of data anomalies by comparing anomaly scores with the threshold. The effectiveness of the proposed method are validated through one simulation experiment and one real-world example. The F1 Score of this method on the simulated missing data and drifting data is 0.9874 and 0.9838. On the drive-end and fan-end is $\mathbf{0. 9 7 0 0}$ and 0.9882 at Case Western Reserve University (CWRU). Compared to other dirty data identification methods in the field, this method has better dirty data identification capability.

关键词： Condition monitoring Correlation Buildings Production Transformers Generative adversarial networks Market research Data models Monitoring Industrial Internet of Things

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Secure Access for Massive Devices in the Industrial Internet of Things (IIoT) 4

Secure Access for Massive Devices in the Industrial Internet...

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2023 4th International Conference on Computer Engineering and Intelligent Control, ICCEIC 2023

作者： Zhang, Bo Xi, Zesheng Zhou, Jieying He, Chuan Lu, Bo Wang, Yunfan State Grid Smart Grid Research Institute Co. Ltd State Grid Key Laboratory of Information and Network Nanjing China State Grid Corporation of China Beijing China State Grid Hunan Electric Power Company Limited Information and Communication Branch Changsha China

ISBN: (纸本)9798350308877

In the context of the widespread adoption of Industrial Internet of Things (IIoT) technology, the number of IoT devices is rapidly increasing, leading to a significant security concern in the form of device impersonation attacks. LoRa devices, as common wireless IoT terminal devices, play a critical role in ensuring the security of the IoT ecosystem through their identity recognition mechanisms. This paper presents a method for secure access of massive IoT devices based on LoRa wireless fingerprints. The proposed method extracts fingerprint features from three types of LoRa devices, which serve as unique wireless identifiers, enabling rapid identification of a large number of LoRa-based IoT devices. The process begins with preprocessing and feature extraction of the collected signals, resulting in three distinctive feature sets that form the wireless fingerprints of the devices. These fingerprints are then used to train a classification model using the K-nearest neighbors (KNN) algorithm. The trained model is subsequently employed to predict the device identity by analyzing the fingerprints of the devices to be identified. Experimental results demonstrate that the proposed system achieves a recognition accuracy of 95.3% under a signal-to-noise ratio of 55dB, while requiring a minimal training time of only 0.3169 seconds. © 2023 IEEE.

关键词： Internet of things

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