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SSRN 2025年

作者： Yu, Zhihao Jin, Yujie Xu, Yongxin Zhang, Xiaoyun Wang, Yasha School of Computer Science Peking University Beijing100091 China National Research and Engineering Center of Software Engineering Beijing100091 China School and Hospital of Stomatology National Center for Stomatology National Clinical Research Center for Oral Diseases National Engineering Research Center of Oral Biomaterials and Digital Medical Devices Beijing100081 China Key Laboratory of High Confidence Software Technologies Ministry of Education Beijing100091 China Peking University Information Technology Institute Tianjin300450 China

While pioneering deep learning methods have made great strides in analyzing electronic health record (EHR) data, they often struggle to fully capture the semantics of diverse medical codes from limited data. The integration of external knowledge from Large Language Models (LLMs) presents a promising avenue for improving healthcare predictions. However, LLM analyses may exhibit significant variance due to ambiguity problems and inconsistency issues, hindering their effective utilization. To address these challenges, we propose IntelliCare, a novel framework that leverages LLMs to provide high-quality patient-level external knowledge and enhance existing EHR models. Concretely, IntelliCare identifies patient cohorts and employs task-relevant statistical information to augment LLM understanding and generation, effectively mitigating the ambiguity problem. Additionally, it refines LLM-derived knowledge through a hybrid approach, generating multiple analyses and calibrating them using both the EHR model and perplexity measures. Experimental evaluations on three clinical prediction tasks across two large-scale EHR datasets demonstrate that IntelliCare delivers significant performance improvements to existing methods, highlighting its potential in advancing personalized healthcare predictions and decision support systems. Our code is available at https://***/yzhHoward/IntelliCare. © 2025, The Authors. All rights reserved.

关键词： Electronic health record

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Global Spatial-Temporal Information-based Residual ConvLSTM for Video Space-Time Super-Resolution

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IEEE Transactions on Multimedia 2025年

作者： Fu, Congrui Yuan, Hui Jiang, Shiqi Zhang, Guanghui Shen, Liquan Hamzaoui, Raouf Center of Experimental Teaching China Shandong University School of Control Science and Engineering China Ministry of Education Key Laboratory of Machine Intelligence and System Control China Shandong University School of software Shandong Jinan China Shandong University School of Computer Science and Technology China Shanghai University School of Communication and Information Engineering Shanghai China De Montfort University School of Engineering and Sustainable Development LeicesterLE1 9BH United Kingdom

By converting low-frame-rate, low-resolution videos into high-frame-rate, high-resolution ones, space-time video super-resolution techniques can enhance visual experiences and facilitate more efficient information dissemination. We propose a convolutional neural network (CNN) for space-time video super-resolution, namely GIRNet. Our method combines long-term global information and short-term local information from the video to better extract complete and accurate spatial-temporal information. To generate highly accurate features and thus improve performance, the proposed network integrates a feature-level temporal interpolation module with deformable convolutions and a global spatial-temporal information-based residual convolutional long short-term memory (convLSTM) module. In the feature-level temporal interpolation module, we leverage deformable convolution, which adapts to deformations and scale variations of objects across different scene locations. This provides a more efficient solution than conventional convolution for extracting features from moving objects. Our network effectively uses forward and backward feature information to determine inter-frame offsets, leading to the direct generation of interpolated frame features. In the global spatial-temporal information-based residual convLSTM module, the first convLSTM is used to derive global spatial-temporal information from the input features, and the second convLSTM uses the previously computed global spatial-temporal information feature as its initial cell state. This second convLSTM adopts residual connections to preserve spatial information, thereby enhancing the output features. Experiments on the Vimeo90K dataset show that the proposed method outperforms open source state-of-the-art techniques in peak signal-to-noise-ratio (by 1.45 dB, 1.14 dB, and 0.2 dB over STARnet, TMNet, and 3DAttGAN, respectively), structural similarity index(by 0.027, 0.023, and 0.006 over STARnet, TMNet, and 3DAttGAN, respectiv

关键词： Long short-term memory

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WMAJL: Watcher-Mediated Attention Joint Learning Model for Multimodal Relation Extraction

WMAJL: Watcher-Mediated Attention Joint Learning Model for M...

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

作者： Yunrui Dong Guiduo Duan Tianxi Huang Yunhao Li School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Laboratory of Intelligent Collaborative Computing University of Electronic Science and Technology of China Chengdu China Trusted Cloud Computing and Big Data Key Laboratory of Sichuan Province Chengdu China Chengdu Textile College College of Humanities and General Education Chengdu China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In the domain of Multimodal Relation Extraction (MRE), we present the $\color{Red}{\text{W}}$atcher-$\color{Red}{\text{M}}$ediated $\color{Red}{\text{A}}$ttention $\color{Red}{\text{J}}$oint $\color{Red}{\text{L}}$earning Model ($\color{Red}{\text{WMAJL}}$), a novel approach addressing the challenges of modality alignment noise, cross-modal fusion disparity, preservation of textual relative position information, and the distinctiveness of classification labels. WMAJL employs an integrative framework leveraging contrastive learning and variational autoencoder constraints to mitigate modality alignment noise by prioritizing relevant semantic data and effectively reducing extraneous noise that does not contribute to the task. The model’s innovative architecture includes a mediator watcher, which facilitates enhanced cross-modal fusion by enabling nuanced information exchange between textual and visual modalities while preserving the unique characteristics of each modality. Additionally, the design of auxiliary tasks, such as Named Entity Recognition (NER), and output supervision constructs loss functions that preserve relative position information, ensuring a precise depiction of entity relationships throughout the multilayer encoding processes. A key differentiator of WMAJL is its label-centric self-information loss technique, inspired by InfoNCE, which trains the model to cluster similar relation labels in semantically coherent areas, thereby optimizing classification label uniqueness by discerning subtle differences among relation types. The synergistic application of these strategies has led to a significant enhancement of WMAJL’s performance, as evidenced by its state-of-the-art F1 score of $\color{Red}{84.93\%}$ on the MNRE dataset. This achievement surpasses existing benchmarks and sets a new standard for multimodal knowledge extraction, underscoring WMAJL’s potential to revolutionize the MRE landscape.

关键词： Resistance Visualization Technological innovation Noise Autoencoders Semantics Contrastive learning Nonhomogeneous media Speech processing Standards

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PEFN: A Patches Enhancement and Hierarchical Fusion Network for Robust Vehicle Re-Identification

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IEEE Internet of Things Journal 2025年

作者： He, Wenying Wang, Feiyu Bai, Yude Xiong, Neal N. Xu, Guangquan Guo, Fei Hebei University of Technology School of Artificial Intelligence Tianjin300401 China Hebei University of Technology Hebei Province Key Laboratory of Big Data Calculation Tianjin300130 China Tiangong University School of Software Tianjin300387 China Northeastern State University Department of Mathematics and Computer Science TahlequahOK74464 United States Tianjin300350 China Central South University School of Computer Science and Engineering Changsha410083 China

Vehicle Re-Identification (Re-ID), which is a significant application in the Internet of Things, aims to accurately retrieve the remaining images of a given vehicle across different cameras views. The improvement in vehicle Re-ID performance largely stems from better addressing the issues of inter-class similarity and intra-class variance. Existing methods, relying solely on max or average pooling after using attention modules, fail to obtain significantly complete and pure global and local features, and neglect the false guidance that some unique individual information on images bring to re-identification. Moreover, models combining global and local features have shown good results in vehicle Re-ID, but these successes neglect the interaction between features across different convolutional layers, resulting in the loss of crucial details for vehicle Re-ID. To tackle these issues, we introduce a Patches Enhancement and hierarchical Fusion Network (PEFN) based on a multi-branch architecture, divided into a Global and Local Attention Supplement (GLAS) branch, and an Enhanced Hierarchical feature fusion (EnHi) branch. The GLAS branch, through the Identity-related Feature Remodeling (IDFR) module’s staged supplementation of spatial and channel features, has achieved the enhancement of both global and local features and effectively mitigated the negative impacts of individual information. The EnHi branch enhances the robustness of feature representation by interacting hierarchical features. Extensive experiments on two large-scale vehicle re-identification datasets demonstrate that our PEFN method outperforms state-of-the-art vehicle re-identification approaches. Specifically, without utilizing extra data and re-ranking, our model achieves 85.15% mAP on the VeRi776 dataset. © 2014 IEEE.

关键词： Reamers

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Anonymous handover authentication protocol for mobile wireless networks with conditional privacy preservation

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Science China(Information Sciences) 2017年第5期60卷 113-129页

作者： Debiao HE Ding WANG Qi XIE Kefei CHEN State Key Laboratory of Software Engineering Computer School Wuhan University State Key Laboratory of Cryptology School of Electronics Engineering and Computer Science Peking University Hangzhou Key Laboratory of Cryptography and Network Security Hangzhou Normal University

With the development of the wireless communication technology and the popularity of mobile devices, the mobile wireless network(MWN) has been widely used in our daily life. Through the access point(AP), users could access the Internet anytime and anywhere using their mobile devices. Therefore, MWNs can bring much convenience to us. Due to the limitation of AP’s coverage, the seamless handover frequently occurs in practical applications. How to guarantee the user’s privacy and security and identify the real identity when he/she brings harm to the system becomes very challenging. To achieve such goals, many anonymous handover authentication(AHA) protocols have been proposed in the last several years. However, most of them have high computation costs because mobile nodes need to carry out the bilinear pairing operations or the hash-to-point operations. Besides, most of them cannot satisfy some critical requirements, such as non-traceability and perfect forward secrecy. In this paper, we first outline the security requirements of AHA protocols, and then propose a new AHA protocol to eliminate weaknesses existing in previous AHA protocols. Based on the hardness of two famous mathematical problems, we demonstrate that the proposed AHA protocol is secure against different kinds of attacks and can meet a variety of security requirements. It can be seen from the details of implementations that the proposed AHA protocol also has much less computation cost than three latest AHA protocols.

关键词： mobile wireless network handover authentication anonymity conditional privacy preservation provable security

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Efficient adaptive test case selection for DNNs robustness enhancement

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Journal of Systems and software 2025年 229卷

作者： Zhang, Zhiyi Meng, Huanze Ding, Yuchen Chen, Shuxian Yao, Yongming Collage of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Jiangsu Nanjing China Collaborative Innovation Center of Novel Software Technology and Industrialization Jiangsu Nanjing China School of Mathematical Science Yangzhou University Jiangsu Yangzhou China Ministry Key Laboratory for Safety-Critical Software Development and Verification Nanjing University of Aeronautics and Astronautics Jiangsu Nanjing China Army Engineering of PLA Jiangsu Nanjing China

Deep neural networks (DNNs) have been widely used in various fields, and testing for DNN-based software has become increasingly important. To discover potential faults in DNNs, a large number of test cases and their corresponding labels are required. However, labeling so many test cases consumes enormous costs. Although there have been many test case selection techniques for DNN models, these techniques still have problems such as high overhead, low efficiency, and poor diversity. To address this problem, this paper proposes an efficient adaptive test case selection method based on the principle of uniform distribution of test cases called EATS. Based on the idea of adaptive testing, EATS combines the uncertainty of the model and the diversity of faults to calculate the distance of test cases and sort them, then gives priority to test cases with a higher probability of causing faults. We conduct experiments on four popular datasets and four representative DNN models. Experiment results show that, compared with the existing eight methods, EATS performs better in uniformity of test case distribution, diversity of errors found, model optimization, and optimization efficiency. © 2025 Elsevier Inc.

关键词： software testing

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FLSSM: A Federated Learning Storage Security Model with Homomorphic Encryption

arXiv

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arXiv 2025年

作者： Li, Yang Xia, Chunhe Li, Chang Li, Xiaojian Wang, Tianbo School of Computer Science and Engineering Beihang University Beijing100191 China Key Laboratory of Beijing Network Technology Beihang University Beijing100191 China Guangxi Collaborative Innovation Center Multi-Source Information Integration and Intelligent Processing Guangxi Normal University Guilin541004 China School of Computer Science and Technology Zhengzhou University of Light Industry Zhengzhou450000 China College of Computer Science and Information Technology Guangxi Normal University Guilin541004 China School of Cyber Science and Technology Beihang University Beijing100191 China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai201112 China

Federated learning based on homomorphic encryption has received widespread attention due to its high security and enhanced protection of user data privacy. However, the characteristics of encrypted computation lead to three challenging problems: "computation-efficiency", "attack-tracing" and "contribution-assessment". The first refers to the efficiency of encrypted computation during model aggregation, the second refers to tracing malicious attacks in an encrypted state, and the third refers to the fairness of contribution assessment for local models after encryption. This paper proposes a federated learning storage security model with homomorphic encryption (FLSSM) to protect federated learning model privacy and address the three issues mentioned above. First, we utilize different nodes to aggregate local models in parallel, thereby improving encrypted models’ aggregation efficiency. Second, we introduce trusted supervise nodes to examine local models when the global model is attacked, enabling the tracing of malicious attacks under homomorphic encryption. Finally, we fairly reward local training nodes with encrypted local models based on trusted training time. Experiments on multiple real-world datasets show that our model significantly outperforms baseline models in terms of both efficiency and security metrics. Copyright © 2025, The Authors. All rights reserved.

关键词： Federated learning

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MLFuse: Multi-Scenario Feature Joint Learning for Multi-Modality Image Fusion

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IEEE Transactions on Multimedia 2025年

作者： Lei, Jia Li, Jiawei Liu, Jinyuan Wang, Bin Zhou, Shihua Zhang, Qiang Wei, Xiaopeng Kasabov, Nikola K. Dalian University Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian116622 China University of Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China Dalian University of Technology School of Mechanical Engineering Dalian116024 China Dalian University of Technology School of Computer Science and Technology Dalian116024 China Auckland University of Technology Knowledge Engineering and Discovery Research Institute Auckland1010 New Zealand Ulster University Intelligent Systems Research Center LondonderryBT52 1SA United Kingdom

Multi-modality image fusion (MMIF) entails synthesizing images with detailed textures and prominent objects. Existing methods tend to use general feature extraction to handle different fusion tasks. However, these methods have difficulty breaking fusion barriers across various modalities owing to the lack of targeted learning routes. In this work, we propose a multi-scenario feature joint learning architecture, MLFuse, that employs the commonalities of multi-modality images to deconstruct the fusion progress. Specifically, we construct a cross-modal knowledge reinforcing network that adopts a multipath calibration strategy to promote information communication between different images. In addition, two professional networks are developed to maintain the salient and textural information of fusion results. The spatial-spectral domain optimizing network can learn the vital relationship of the source image context with the help of spatial attention and spectral attention. The edge-guided learning network utilizes the convolution operations of various receptive fields to capture image texture information. The desired fusion results are obtained by aggregating the outputs from the three networks. Extensive experiments demonstrate the superiority of MLFuse for infrared-visible image fusion and medical image fusion. The excellent results of downstream tasks (i.e., object detection and semantic segmentation) further verify the high-quality fusion performance of our method. The code is publicly available at https://***/jialei-sc/MLFuse. © 1999-2012 IEEE.

关键词： Semantic Segmentation

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Adaptive Latitude-Aware and Importance-Activated Transform Coding for Learned Omnidirectional Image Compression

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IEEE Transactions on Broadcasting 2025年

作者： Hu, Hui Shi, Yunhui Wang, Jin Ling, Nam Yin, Baocai Beijing University of Technology Beijing Key Laboratory of Multimedia and Intelligent Software Technology Beijing Institute of Artificial Intelligence School of Information Science and Technology Beijing 100124 China Santa Clara University Department of Computer Science and Engineering Santa Clara 95053 CA United States

Based on the measured latitude and longitude, users can freely view different perspectives of the omnidirectional image. Typically, omnidirectional images are represented in the equirectangular projection (ERP) format. Although ERP images suffer from distortion and redundancy due to oversampling, making traditional codec inefficient, they maintain visual consistency and enhance compatibility with deep learning-based image processing tools. This has led to the emergence of end-to-end omnidirectional image compression methods based on the ERP format. In fact, transform coding, a key component in learned planar image compression, has not yet been fully explored in the domain of learned omnidirectional image compression. In this paper, we propose a transform coding method with adaptive latitude-aware and importance-activated features for omnidirectional image compression. Specifically, the adaptive latitude-aware mechanism comprises two modules. The first module, termed Adaptive Latitude-aware Module (ALAM), employs rectangular dilated convolutional kernels of multiple sizes to perceive distortion redundancy across different latitudes, followed by latitude-adaptive weighting to select optimal features for respective latitudes. The second module, named Multi-scale Convolutional Gated Feedforward Network (MCGFN), fully exploits local contextual information while suppressing feature redundancy induced by diverse dilated convolutions in the first module. Furthermore, to further reduce ERP redundancy, we design an importance-activated spatial feature transform module that regulates latent representations to allocate more bits to significant regions. Experimental results demonstrate that our proposed method outperforms existing VVC standards and learning-based omnidirectional image compression approaches at medium-to-high bitrates while maintaining low computational complexity. © 1963-12012 IEEE.

关键词： equirectangular projection (ERP) importance activation latitude adaptive transform coding Omnidirectional image compression

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A Lightweight Model for Perceptual Image Compression via Implicit Priors

arXiv

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arXiv 2025年

作者： Wei, Hao Zhou, Yanhui Jia, Yiwen Ge, Chenyang Anwar, Saeed Mian, Ajmal National Key Laboratory of Human-Machine Hybrid Augmented Intelligence Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’an710049 China School of Information and Telecommunication Xi’an Jiaotong University Xi’an710049 China Department of Computer Science and Software Engineering The University of Western Australia Crawley PerthWA6009 Australia

Perceptual image compression has shown strong potential for producing visually appealing results at low bitrates, surpassing classical standards and pixel-wise distortion-oriented neural methods. However, existing methods typically improve compression performance by incorporating explicit semantic priors, such as segmentation maps and textual features, into the encoder or decoder, which increases model complexity by adding parameters and floating-point operations. This limits the model’s practicality, as image compression often occurs on resource-limited mobile devices. To alleviate this problem, we propose a lightweight perceptual Image Compression method using Implicit Semantic Priors (ICISP). We first develop an enhanced visual state space block that exploits local and global spatial dependencies to reduce redundancy. Since different frequency information contributes unequally to compression, we develop a frequency decomposition modulation block to adaptively preserve or reduce the low-frequency and high-frequency information. We establish the above blocks as the main modules of the encoder-decoder, and to further improve the perceptual quality of the reconstructed images, we develop a semantic-informed discriminator that uses implicit semantic priors from a pretrained DINOv2 encoder. Experiments on popular benchmarks show that our method achieves competitive compression performance and has significantly fewer network parameters and floating point operations than the existing state-of-the-art. We will release the code and trained models. Copyright © 2025, The Authors. All rights reserved.

关键词： Decoding

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