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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yang Liu Weiyu Zhang Jiasheng Si Zhao Li Xueping Peng Wenpeng Lu 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 Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China Evay Info Jinan China Australian Artificial Intelligence Institute University of Technology Sydney Sydney Australia

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Electronic health records (EHRs) play a crucial role in the development of personalized treatment plans for patients. However, EHRs are often highly incomplete, posing significant challenges for predictive modeling. While existing deep learning models employ various imputation techniques to reconstruct missing values, they often fail to represent missing data in a personalized manner and do not learn from the missingness patterns in EHRs data. This limitation reduces their effectiveness in practical personalized healthcare applications. To address this issue, we propose SPIME, a self-supervised model that generates personalized imputations in EHRs data for patient health status prediction. We introduce a personalized missing mask (PMM) based on the frequency of feature measurements. Additionally, we incorporate a masked imputation task (MIT) loss that minimizes the loss of artificially introduced missing values, thereby enhancing the model’s capability to handle missing data. SPIME adopts self-supervised pretraining to learn representations from personalized missing patterns and reconstructs missing data in the latent space. To further enhance representation learning, two independent attention mechanisms are applied separately across the feature and temporal dimensions. Experimental results on two real-world EHRs datasets show that SPIME outperforms existing state-of-the-art methods in predicting in-hospital mortality and decompensation, demonstrating its effectiveness in reconstructing missing data and predicting patient health status. The code will be published at https://***/cling6666/SPIME.

关键词： Training Representation learning Biological system modeling Mortality Predictive models Biomedical measurement Imputation Data models Frequency measurement Electronic medical records

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RoPA: Robust Privacy-Preserving Forward Aggregation for Split Vertical Federated Learning

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IEEE Transactions on network and Service Management 2025年

作者： Wang, Lingling Zhang, Zhengyin Huang, Mei Gai, Keke Wang, Jingjing Shen, Yulong Qingdao University of Science and Technology School of Information Science and Technology Shandong Qingdao266061 China Beijing Institute of Technology School of Cyberspace Science and Technology Beijing100081 China Xidian University School of Computer Science and Technology China Shaanxi Key Laboratory of Network and System Security Xi’an710071 China

Split Vertical Federated Learning (Split VFL) is an increasingly popular framework for collaborative machine learning on vertically partitioned data. However, it is vulnerable to various attacks, resulting in privacy leakage and robust aggregation issues. Recent works have explored the privacy protection of raw data samples and labels, neglecting malicious attacks launched by dishonest passive parties. Since they may deviate from the protocol and launch embedding poisoning attacks and free-riding attacks, it will inevitably result in model performance loss. To address this issue, we propose a Robust Privacy-preserving forward Aggregation (RoPA) protocol, which can resist embedding poisoning attacks and free-riding attacks and protect the privacy of embedding vectors. Specifically, we first present a modified Secret-shared Non-Interactive Proofs (SNIP) algorithm to guarantee the integrity verification of embedding vectors. To prevent free-riding attacks, we also give a validity verification protocol using matrix commitment. In particular, we utilize probability checking and batch verification to improve the verification efficiency of the protocol. Moreover, we adopt arithmetic secret sharing to protect data privacy. Finally, we conduct rigorous theoretical analysis to prove the security of RoPA and evaluate the performance of RoPA. The experimental results show that the proof verification overhead of RoPA is approximately 8× lower than the original SNIP, and the model accuracy is improved by ranging from 3% to 15% under the above two malicious attacks. © 2004-2012 IEEE.

关键词： Federated learning

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Cross Aggregation network for Semantic Segmentation

SSRN

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SSRN 2022年

作者： Zhao, Minghua Zhi, Yuxing Du, Shuangli Hei, Xinhong Hu, Jing Shi, Cheng Li, Peng School of Computer Science and Engineering Xi’an University of Technology No.5 South Jinhua Road Shaanxi Xi’an710048 China Shaanxi Key Laboratory of Network Computing and Security Technology No.5 South Jinhua Road Shaanxi Xi’an710048 China

To achieve more accurate prediction, advanced semantic segmentation methods are explored in the way of context modeling. Images in real scenes usually contain multi-scale objects and contents. The feature propagation form in convolution networks is very important to capture multi-scale context and obtain accurate segmentation. This paper proposes a novel pattern of information flow aggregation to rich features expression, called Cross Aggregation Module (CRA), In CRA, the flows of different scales are transmitted to the context aggregation module (CAM) through a parallel-cross connection to generate a feature map containing more information to aggregate multi-scale features and capture remote context information. We further add the low-level features in the later stage in the encoder to enhance the features interaction. Based on these developments, we build a Context Cross Aggregation network (CRANet) which employs an asymmetric decoder to restore the scale of the predicted feature map. The proposed CRANet is evaluated on two challenging datasets, i.e., Cityscapes and Camvid. The experiments show that the proposed network achieves competitive performance. © 2022, The Authors. All rights reserved.

关键词： Semantic Segmentation

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MMSFD: Multi-grained and Multi-modal Fusion for Short Video Fake News Detection

MMSFD: Multi-grained and Multi-modal Fusion for Short Video ...

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Data Science and Information technology (DSIT), International Conference on

作者： Shuai Ren Yahui Liu Yaping Zhu Wanlong Bing Hongliang Ma Wei Wang School of Information Science and Technology Shihezi University Shihezi China Key Laboratory of Computing Intelligence and Network Information Security Xinjiang Production and Construction Corps Shihezi China School of Computer Science and Information Technology Beijing Jiaotong University Beijing China

ISBN: (数字)9798350384093

ISBN: (纸本)9798350384109

Short video platforms have become an essential part of people’s daily lives, while also facing the challenge of the rapid spread of fake news. Therefore, the detection of fake news in short videos has attracted increasing attention. Existing works mostly focus on the integration of textual and visual content, but fail to fully exploit multi-modal information at both fine-grained and coarse-grained levels. Moreover, multi-modal fake news detection is vulnerable to modality ambiguity due to potential conflicts or weak correlations between different modalities. In this paper, we propose a Multi-grained and Multi-modal fusion for Short video Fake news Detection (MMSFD). We encode textual and visual content using pre-trained models at both fine-grained and coarse-grained levels and fuse them through distinct branches. To tackle the ambiguity issue, we design a multi-modal fusion branch based on similarity weighting, as well as separate single-modal branches. Additionally, this paper presents a cross-modal attention mechanism for adaptively aggregating and reweighting features. The experimental results on a real-world short video dataset demonstrate that the proposed method outperforms state-of-the-art short video fake news detection methods.

关键词： Visualization Adaptation models Attention mechanisms Quantization (signal) Computational modeling Feature extraction Rendering (computer graphics) Encoding Real-time systems Fake news

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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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Prompt and Contrastive Learning for Few-shot Sentiment Classification 22

Prompt and Contrastive Learning for Few-shot Sentiment Class...

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Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition

作者： Fei Wang Long Chen Xiaohua Huang Cai Xu Wei Zhao Ziyu Guan Guangyue Lu Shaanxi Key Laboratory of Information Communication Network and Security Xi'an University of Posts and Telecommunications China School of Information Science and Technology Northwest University China School of Computer Science and Technology Xidian University China

ISBN: (纸本)9781450396899

Sentiment classification is a hot topic in the field of natural language processing. Currently, state-of-the-art classification models follow two steps: pre-training a large language model on upstream tasks, and then using human-labeled data to fine-tune a task-related model. However, there is a large gap between the upstream tasks of the pre-trained model and the downstream tasks being performed, resulting in the need for more labeled data to achieve excellent performance. Manually annotating data is expensive. In this paper, we propose a few-shot sentiment classification method based on Prompt and Contrastive Learning (PCL), which can significantly improve the performance of large-scale pre-trained language models in low-data and high-data regimes. Prompt learning aims to alleviate the gap between upstream and downstream tasks, and the contrastive learning is designed to capture the inter-class and intra-class distribution patterns of labeled data. Thanks to the integration of the two strategies, PCL markedly exceeds baselines with low resources. Extensive experiments on three datasets show that our method has outstanding performance in the few-shot settings.

关键词： Contrastive learning Prompt learning Sentiment classification

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Retraction Note: Deep learning method for traffic accident prediction security (Soft computing, (2022), 26, 11, (5363-5375), 10.1007/s00500-022-07096-7)

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Soft computing 2024年第2期28卷 971-971页

作者： Tian, Zhun Zhang, Shengrui College of Transportation Engineering Chang’an University Shaanxi Xi’an710064 China School of Civil Engineering Xi’an University of Architecture and Technology Shaanxi Xi’an710055 China Key Laboratory of Transport Industry of Management Control and Cycle Repair Technology for Traffic Network Facilities in Ecological Security Barrier Area Chang’an University Shaanxi Xi’an710064 China

The publisher has retracted this article in agreement with the Editor-in-Chief. The article was submitted to be part of a guest-edited issue. An investigation by the publisher found a number of articles, including this one, with a number of concerns, including but not limited to compromised editorial handling and peer review process, inappropriate or irrelevant references or not being in scope of the journal or guest-edited issue. Based on the investigation’s findings the publisher no longer has confidence in the results and conclusions of this article. Authors Zhun Tian & Shengrui Zhang have not responded to correspondence regarding this retraction. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

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FRCNet: Frequency and Region Consistency for Semi-supervised Medical Image Segmentation

arXiv

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arXiv 2024年

作者： He, Along Li, Tao Wu, Yanlin Zou, Ke Fu, Huazhu Tianjin Key Laboratory of Network and Data Security Technology College of Computer Science Nankai University Tianjin China Haihe Lab of ITAI China National Key Laboratory of Fundamental Science on Synthetic Vision Sichuan University Sichuan Chengdu China Institute of High Performance Computing Agency for Science Technology and Research Singapore

Limited labeled data hinder the application of deep learning in medical domain. In clinical practice, there are sufficient unlabeled data that are not effectively used, and semi-supervised learning (SSL) is a promising way for leveraging these unlabeled data. However, existing SSL methods ignore frequency domain and region-level information and it is important for lesion regions located at low frequencies and with significant scale changes. In this paper, we introduce two consistency regularization strategies for semi-supervised medical image segmentation, including frequency domain consistency (FDC) to assist the feature learning in frequency domain and multi-granularity region similarity consistency (MRSC) to perform multi-scale region-level local context information feature learning. With the help of the proposed FDC and MRSC, we can leverage the powerful feature representation capability of them in an effective and efficient way. We perform comprehensive experiments on two datasets, and the results show that our method achieves large performance gains and exceeds other state-of-the-art methods. © 2024, CC BY.

关键词： Deep learning

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News-driven financial warning based on label information attention 22

News-driven financial warning based on label information att...

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Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition

作者： Ren Zhilin Chen Long Huang Xiaohua Wang Wenjing Xu Cai Zhao Wei Guan Ziyu Shaanxi Key Laboratory of Information Communication Network and Security Xi'an University of Posts and Telecommunications China School of Information Science and Technology Northwest University China School of Computer Science and Technology Xidian University China

ISBN: (纸本)9781450396899

Most existing news-driven stock market prediction methods ignore the potential relationship between financial news and stocks. The complex relationship can help us to improve the accuracy of algorithmic trading systems. Therefore, we propose a deep learning method for financial warning by fusing Stock Label Information (SLI). We extract events from news texts and fuse stock information together as feature vectors, using neural networks to model the underlying relationship between news and stocks. Experimental results show that our method outperforms other baseline methods in experiments on TPX500 and TPX100 *** CONCEPTS • computing methodologies • Artificial intelligence • Natural language processing

关键词： Deep learning Financial warning News-driven Stock label information

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Evolutionary Multitasking Based on Team Learning Strategy

Evolutionary Multitasking Based on Team Learning Strategy

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Artificial Intelligence technology (ACAIT), Asian Conference on

作者： Wei Li Xinyu Gao Lei Wang Qingzheng Xu School of Computer Science and Engineering Xi’an University of Technology Xi’an China Shaanxi Key Laboratory for Network Computing and Security Technology Xi’an China College of Information and Communication National University of Defense Technology Wuhan China

Multi-factorial optimization (MFO) is a popular optimization mechanism recently, which aims to optimize multiple tasks in a single run. Generally, the multi-factorial optimization algorithm uses the random learning strategy to generate offspring, which leads to an inefficient exploration ability of the algorithm. To address this problem, this paper proposed a simple and effective strategy called team learning strategy (TLS). The proposed learning strategy divided the population into excellent team and ordinary team. Different teams employ different learning strategies. Moreover, nine MFO problems were used to assess the effectiveness of the proposed strategy. The experimental results show that the team learning strategy can improve the performance of the algorithm.

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