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Enhancing diabetes complications prediction through knowledge graphs and convolutional networks

engineering Applications of Artificial Intelligence 2025年 153卷

作者： Cheng, Haitao Zheng, Qunli Li, Peng Xu, He School of Computer Science Nanjing University of Posts and Telecommunications Nanjing210023 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China

Early prediction of diabetes complications is crucial for timely intervention and effective disease management. However, current deep learning approaches often lack sufficient representation of diabetes knowledge and rely on large-scale annotated data. To address this, we propose a knowledge Graphs-enhanced Diabetes Complications Prediction (KGDCP) model, which integrates a physical examination knowledge graph and an improved Diabetes knowledge Graph (DiaKG) using self-attention-based convolutional neural networks. We construct the physical examination knowledge graph based on normal reference ranges of examination indicators and enhance DiaKG through knowledge deduplication and coreference resolution, improving its accuracy and comprehensiveness. Furthermore, we employ the embeddings of these two knowledge graphs to represent examination data and diagnostic information. A token-to-token (token2token) self-attention is utilized to explore dependencies among examination indicators, and convolutional neural networks extract local features to generate representation vectors. Additionally, source-to-token (source2token) self-attention assesses dependencies between diagnostic entities and the entire entity set, with entity-to-Physical Examination (entity2PE) attention gauging entity relevance to examination data representation vectors. Our model combines these features to enhance diabetes complications prediction, outperforming traditional models in predicting cerebral infarction and peripheral neuropathy. © 2025

关键词： knowledge graph

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Enabling Generalized Zero-Shot Vulnerability Classification

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IEEE Transactions on Dependable and Secure Computing 2025年

作者： Hu, Jinghao Guo, Jinsong Luo, Chen Hu, Yang Lanzinger, Matthias Li, Zhanshan Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education College of Software Changchun130012 China University College London Department of Computer Science United Kingdom Amazon United States United Kingdom University of Oxford Department of Computer Science United Kingdom Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education College of Computer Science and Technology Changchun130012 China

Regarding computer security, the growth of code vulnerability types presents a persistent challenge. These vulnerabilities, which may cause severe consequences, necessitate precise classification for effective mitigation. However, the rapid emergence of new vulnerability types complicates the classification process. Traditional methodologies, which often involve human expertise and the manual labeling or generation of example instances, are not only resource-intensive but also struggle to adapt to the dynamic nature of these vulnerabilities. This paper introduces VulnSense, an innovative method that harnesses the capabilities of Generalized Zero-Shot Learning (GZSL) to address the vulnerability classification problem. VulnSense learns about unseen vulnerability classes from the descriptions of these unseen classes, while not requiring to see any instances of these unseen classes. Specifically, VulnSense learns from three main resources: (1) seen classes with labeled code instances;(2) descriptions of these seen classes;and (3) descriptions of "unseen"classes which have no labeled instances. Our experiments underscore VulnSense's superiority over existing GZSL methods in classifying instances of unseen classes. Concurrently, it maintains a performance parity with traditional labeled-example based learning methods in classifying instances of seen vulnerabilities. VulnSense demonstrates the potential of using GZSL for vulnerability classification, while also highlighting challenges that inspire future work. © 2004-2012 IEEE.

关键词： Zero-shot learning

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KSSANet: KAN-Driven Spatial-Spectral Attention Networks for Hyperspectral Image Super-Resolution

KSSANet: KAN-Driven Spatial-Spectral Attention Networks for ...

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

作者： Baisong Li Xingwang Wang Haixiao Xu College of Computer Science and Technology Jilin University Key Laboratory of Symbolic Computation and Krowledge Engineering of Ministry of Education Jilin University

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Due to the limitations of physical imaging, acquiring high-resolution hyperspectral images (HR-HSIs) has always been a significant challenge. Single hyperspectral image super-resolution (SHSR) technology aims to generate corresponding HR-HSIs by processing low-resolution hyperspectral images (LR-HSIs). Compared to multi-source data fusion methods, SHSR relies solely on a single low-resolution image and does not require additional auxiliary information or multimodal data, making it more flexible and efficient in data acquisition. Recently, Kolmogorov–Arnold Networks (KAN), which derive from the Kolmogorov–Arnold representation theorem, show great potential in modeling long-range dependencies. In this paper, we further investigate the potential of KAN for hyperspectral image restoration. Specifically, we propose a spatial-spectral attention block (SSAB) module, which includes a KAN-based spatial attention module (KAN-SpaAB) and a KAN-based spectral attention module (KAN-SpeAB), designed for the restoration of spatial and spectral information, respectively. Experimental results demonstrate that KSSANet outperforms existing methods in both quantitative evaluation and image generation quality, achieving state-of-the-art (SOTA) performance. Our code is available at: https://***/Baisonm-Li/KSSANet.

关键词： Measurement Visualization Image synthesis Superresolution Imaging Image restoration Speech processing Spatial resolution Signal resolution Hyperspectral imaging

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SSRMamba: Efficient Visual State Space Model for Spectral Super-Resolution

SSRMamba: Efficient Visual State Space Model for Spectral Su...

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

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Spectral super-resolution, which reconstructs hyperspectral images (HSI) from a single RGB image, has garnered increasing attention. Due to the limitations of CNN structures in spectral modeling and the high computational cost of Transformer structures, existing deep learning (DL)-based methods struggle to balance spectral reconstruction quality and computational efficiency. Recently, Mamba methods base on state-space models (SSM) show great potential in modeling long-range dependencies with linear complexity. Therefore, we introduce the Mamba model into spectral super-resolution (SSR) task. Specifically, we propose a three-stage SSR network base on Mamba, called SSRMamba. We design SpaMamba, SSMamba, and SpeMamba modules for shallow spatial information extraction, mixed information encoding, and spectral information reconstruction, respectively. Extensive experimental results demonstrate that SSRMamba not only surpasses existing methods in terms of quantification and quality, achieving state-of-the-art (SOTA) performance, but also significantly reduces model size and computational cost. The source code of SSRMamba is available at: https://***/Baisonm-Li/SSRMamba.

关键词： Visualization computational modeling Superresolution Information retrieval Transformers computational efficiency State-space methods Speech processing Image reconstruction Hyperspectral imaging

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VECLLF: A vehicle-edge collaborative lifelong learning framework for anomaly detection in VANETs

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Computer Networks 2025年 265卷

作者： Wang, Yingqing Liang, Yanhua Huang, Yue Qin, Guihe Jilin University College of Computer Science and Technology Changchun130012 China Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Changchun130012 China Jilin University Center for Computer Fundamental Education Changchun130012 China

With the rapid development of intelligent connected vehicles, Vehicular Ad Hoc Network (VANET) is widely used in intelligent transportation systems. VANETs are vulnerable to attacks in their operating environments, such as denial of service (DoS) attacks and injection attacks. Thus, the development of an efficient anomaly detection framework is a promising solution. Most existing methods can only detect known attacks, are less efficient in detecting unknown attacks, and fail to deal with them. To solve these problems, the lifelong learning framework (LLF) is introduced, which aims to store and label suspicious data by applying blockchain and then retrain the model. However, existing LLFs are overly idealized as they do not adequately consider the key issues of label budget and storage space. To improve the practicality of existing methods, this paper proposes a novel LLF based on the existing LLF, called Vehicle Edge Collaborative Lifelong Learning Framework (VECLLF), which can deploy the model update task on the edge server. Specifically, the proposed anomaly detection module based on ensemble active learning is used to achieve high-performance detection under a limited label budget, the proposed incremental learning module based on a sample library continuously learns the model by replaying representative samples within limited storage space, the proposed optimization-based feature selection module is used to remove redundant and interfering features, and the explainable artificial intelligence (XAI) module is used to explain the model to ensure its interpretability. In addition, the blockchain-based database (BC-DB) module is used to help store suspicious data and interact with security agencies to label it. We conducted experiments on two public datasets, AWID2 and CICIDS2017, and the results show that VECLLF outperforms state-of-the-art anomaly detection models and existing LLF, with F1-scores exceeding 97% on both datasets. Furthermore, we collect data from th

关键词： Rapid transit

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Prompt-guided orthogonal multimodal fusion for cancer survival prediction

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Information Sciences 2025年 715卷

作者： Huang, Lan Guo, Shuyu Bai, Tian Zhao, Ruihong Tao, Ke College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China Department of Gastroenterology The First Hospital of Jilin University Changchun130000 China

Cancer survival prediction can assist clinicians in developing personalized treatment plans for patients. Comprehensive cancer diagnosis and treatment require integrating macroscopic and microscopic imaging. However, significant discrepancies in the spatial resolution and anatomical scale between imaging modalities hinder existing multimodal fusion methods from effectively learning correlated semantic features with limited datasets. In this work, we introduce a prompt-guided orthogonal multimodal fusion strategy (POMF) for fusing multimodal medical images across anatomical scales. POMF utilizes modality-specific prompts to fine-tune pretrained models, facilitating bias adaptation to medical imaging features while ensuring more computationally efficient training. A modality consistency-discrepancy prototype is designed as the modality-inherent prompt in POMF, disentangling the multimodal features and bridging the potential correlations across the orthogonal modalities. POMF is validated on a glioma survival prediction task using paired radiology and pathology images. The experiment results suggest that POMF achieves superior C-index with existing full-tuning and prompt-tuning methods. Additionally, the ablation studies demonstrate that POMF is adaptable to various architectures of pretrained encoders and multiple multimodal fusion strategies on cross-scale medical images. © 2025 Elsevier Inc.

关键词： Mammography

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Rethinking Polyp Segmentation from the Perspectives of Matching Views and Seeking Camouflage

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Multimedia Systems 2025年第3期31卷 1-17页

作者： Jiang, Zhengfang Chen, Haipeng Yang, Yongping Liu, Xianzhu Lyu, Yingda College of Software Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China College of Computer Science and Technology Jilin University Changchun130012 China Department of Colorectal Anal Surgery The Second Hospital of Jilin University Changchun130022 China National and Local Joint Engineering Research Center of Space Optoelectronics Technology Changchun University of Science and Technology Changchun130022 China College of Opto-Electronic Engineering Changchun University of Science and Technology Changchun130022 China Public Computer Education and Research Center Jilin University Changchun130012 China

Accurate segmentation of polyps from colonoscopy images is of great significance for the prevention of colorectal cancer. Despite notable progress, polyp segmentation remains a challenging task due to the diversity of polyp size and shape, as well as the camouflage property of polyps caused by their high similarity to surrounding tissues. To address these challenges, inspired by human perceptual experience in finding objects, we propose a multi-perspective collaborative network (MPCNet), innovatively identifying the potential polyps from the perspectives of matching views and seeking camouflage. Firstly, from the former perspective, a global–local aware module is proposed to provide various feature receptive fields to match suitable views for polyps of different sizes and shapes. Then, from the latter perspective, a texture enhancement module (TEM) and an interaction refinement module (IRM) are proposed to mine and complement clues essential for breaking the camouflage. Specifically, TEM mines texture-related clues and amplifies the subtle texture differences between polyps and their background, while IRM complements boundary clues and further optimizes feature representation through the interaction between boundaries and regions, thereby generating accurate segmentation results with complete regions and fine boundaries. Extensive experiments on five benchmark polyp datasets show that our MPCNet outperforms other state-of-the-art methods. In particular, we achieve a mean Dice of 81.7% and 82.4% on the most challenging datasets CVC-ColonDB and ETIS, respectively, which are significantly higher than competing methods. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Image segmentation

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Efficient Sharing of Energy Consumption Data: A Privacy-Preserving Threshold Aggregation Approach

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IEEE Internet of Things Journal 2025年

作者： Li, Guohao Zhou, Lu Lian, Jiale Liu, Siyi Yang, Li Zhong, Yantao Li, Qiang Xidian University School of Computer Science and Technology Xi’an China Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Changchun China Co. Ltd Guangdong Shenzhen China

Energy consumption data collected by smart meters is increasingly used by various subscribers in the smart grid for load management, energy monitoring, and policy planning. To protect user privacy, edge-assisted privacy-preserving data aggregation (PPDA) techniques are commonly employed. However, existing methods face several challenges: i) limited scalability, ii) strict trust requirements, and iii) the risk of revealing unique consumption patterns to data collectors. To address these challenges, we propose a privacy-preserving threshold aggregation method that is easily scalable and facilitates efficient energy data sharing under limited trust assumptions. Specifically, we design VFPNTRU, a quantum-resistant homomorphic proxy re-encryption scheme with fault tolerance and re-encryption verification. In VFP-NTRU, smart meters can encrypt data with a public key without the need for prior negotiation of decryption keys with multiple subscribers. Additionally, we develop a privacy threshold collection protocol that uses a verifiable oblivious pseudorandom function to provide privacy guarantees similar to k-anonymity for SM data collection. We further introduce an energy consumption model to determine optimal collection strategies, improving system responsiveness. We provide correctness analysis and prove the security of our scheme. Experimental results demonstrate that our approach outperforms existing PPDA methods, making it particularly suitable for resource-constrained smart meters and central servers managing large-scale energy data. © 2014 IEEE.

关键词： Differential privacy

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arXiv

引用

arXiv 2025年

作者： Li, Yangning Kang, Hui Li, Jiahui Sun, Geng Sun, Zemin Wang, Jiacheng Zhao, Changyuan Niyato, Dusit College of Computer Science and Technology Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China College of Computer Science and Technology Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China College of Computing and Data Science Nanyang Technological University 639798 Singapore College of Computing and Data Science Nanyang Technological University Singapore

An expansion of Internet of Things (IoTs) has led to significant challenges in wireless data harvesting, dissemination, and energy management due to the massive volumes of data generated by IoT devices. These challenges are exacerbated by data redundancy arising from spatial and temporal correlations. To address these issues, this paper proposes a novel data-driven collaborative beamforming (CB)-based communication framework for IoT networks. Specifically, the framework integrates CB with an overlap-based multi-hop routing protocol (OMRP) to enhance data transmission efficiency while mitigating energy consumption and addressing hot spot issues in remotely deployed IoT networks. Based on the data aggregation to a specific node by OMRP, we formulate a node selection problem for the CB stage, with the objective of optimizing uplink transmission energy consumption. Given the complexity of the problem, we introduce a softmax-based proximal policy optimization with long-short-term memory (SoftPPO-LSTM) algorithm to intelligently select CB nodes for improving transmission efficiency. Simulation results show that the proposed OMRP improves network lifetime by 17% compared to benchmark routing protocols, while the SoftPPO-LSTM method for CB node selection achieves an 8.3% increase in throughput over benchmark algorithms. The results also reveal that the combined OMRP with the SoftPPO- Copyright © 2025, The Authors. All rights reserved.

关键词： Deep reinforcement learning

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UAV-Assisted Joint Mobile Edge Computing and Data Collection via Matching-Enabled Deep Reinforcement Learning

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IEEE Internet of Things Journal 2025年

作者： Wang, Boxiong Kang, Hui Li, Jiahui Sun, Geng Sun, Zemin Jilin University College of Computer Science and Technology Changchun130012 China Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Changchun130012 China Nanyang Technological University College of Computing and Data Science 639798 Singapore

Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) and data collection (DC) have been popular research issues. Different from existing works that consider MEC and DC scenarios separately, this paper investigates a multi-UAV-assisted joint MEC-DC system. Specifically, we formulate a joint optimization problem to minimize the MEC latency and maximize the collected data volume. This problem can be classified as a non-convex mixed integer programming problem that exhibits long-term optimization and dynamics. Thus, we propose a deep reinforcement learning-based approach that jointly optimizes the UAV movement, user transmit power, and user association in real time to solve the problem efficiently. Specifically, we reformulate the optimization problem into an action space-reduced Markov decision process (MDP) and optimize the user association by using a two-phase matching-based association (TMA) strategy. Subsequently, we propose a soft actor-critic (SAC)-based approach that integrates the proposed TMA strategy (SAC-TMA) to solve the formulated joint optimization problem collaboratively. Simulation results demonstrate that the proposed SAC-TMA is able to coordinate the two subsystems and can effectively reduce the system latency and improve the data collection volume compared with other benchmark algorithms. © 2025 IEEE. All rights reserved,

关键词： Unmanned aerial vehicles (UAV)

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