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Structural Optimization of Causal Driven Model Based on Bayesian Network in High-dimensional Data Classification

Applied Mathematics and Nonlinear Sciences 2025年第1期10卷

作者： Li, Kuo Wang, Aimin Wang, Limin Zhao, Yuetan Zhu, Xinyu College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Changchun130012 China College of Software Jilin University Changchun130012 China

The Bayesian network (BN) model, as a big data graph model that integrates causal inference and probabilistic representation, has received widespread attention and research in both academia and industry. However, with the advent of the big data era, traditional BN structure learning algorithms have encountered unprecedented challenges in processing high-dimensional data, mainly manifested as a sharp increase in computational complexity and difficulty in achieving ideal accuracy requirements within an acceptable time range, which greatly limits their breadth and depth in practical applications. In response to this bottleneck problem, this article innovatively proposes a new approach that combines width learning theory with BN, referred to as Broad Bayesian Neural Network (Broad-BNN). This model effectively reduces the dimensionality of the original high-dimensional data by introducing a feature mapping layer and gradually expanding it, while achieving non-linear transformation of information and effective feature extraction. The experimental results show that the model proposed in this paper has achieved significant performance improvement in high-dimensional data classification problems, not only accelerating training speed but also significantly improving classification accuracy, providing a new perspective and solution for solving the difficulties of high-dimensional data processing. © 2025 Kuo Li et al., published by Sciendo.

关键词： Bayesian networks

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Contextual Graph Reconstruction and Emotional Variation Learning for Conversational Emotion Recognition

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IEEE Transactions on Affective Computing 2025年

作者： Rao, Yujing Cao, Min Ye, Mang Wuhan University National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence Hubei Key Laboratory of Multimedia and Network Communication Engineering School of Computer Science Wuhan 430072 China Soochow University School of Computer Science and Technology Suzhou 215006 China

Conversational Emotion Recognition (CER) significantly benefits from the integration of multiple modalities. However, real-world scenarios are often plagued by hardware malfunctions and network failures that lead to missing modalities and incomplete emotional representations. Existing methods primarily focus on modeling inter-modal relationships within isolated utterances to generate missing data, so they do not adequately capture conversational context and dynamic emotional evolution. In conversations, emotional expressions are inherently context-dependent and dynamic. Insufficient modeling of these properties can substantially degrade performance. To address these challenges, we propose Contextual Graph Reconstruction and Emotional Variation Learning (CGR-EVL). Our approach flexibly constructs an utterance graph with diverse coverage based on speaker activity and modality availability, thereby capturing more contextual information. To enhance the handling of missing modalities, we further integrate temporal relations through a relational graph convolutional network to reconstruct missing features and introduce a speaker emotion-aware constraint to ensure emotional coherence. Additionally, we propose the concept of emotional entropy to quantify variation patterns and develop a novel loss function that aligns predicted and actual variations. Experiments on the IEMOCAP and MELD datasets show that CGR-EVL outperforms state-of-the-art methods, particularly under conditions of incomplete modalities. © 2010-2012 IEEE.

关键词： Emotional variation graph neural network modality reconstruction multimodal emotion recognition

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SMT(LIA) Sampling with High Diversity

arXiv

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arXiv 2025年

作者： Lai, Yong Li, Junjie Luo, Chuan College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Changchun130012 China School of Software Beihang University Beijing100191 China

Satisfiability Modulo Linear Integer Arithmetic, SMT(LIA) for short, is pivotal across various critical domains. Previous research has primarily focused on SMT solving techniques. However, in practical applications such as software and hardware testing, there is a need to generate a diverse set of solutions for use as test inputs. We have developed the first sampling framework that integrates local search with CDCL(T) techniques, named HighDiv, capable of generating a highly diverse set of solutions for constraints under linear integer theory. Initially, in the local search phase, we introduced a novel operator called boundary-aware movement. This operator performs random moves by considering the current state’s constraints on variables, thereby enhancing the diversity of variables during the search process. Furthermore, we have conducted an in-depth study of the preprocessing and variable initialization mechanisms within the framework, which significantly enhances the efficiency of subsequent local searches. Lastly, we use the solutions obtained from local search sampling as additional constraints to further explore the solution space using the stochastic CDCL(T) method. Experimental results demonstrate that HighDiv generates solutions with greater diversity compared to the state-of-the-art SMT(LIA) sampling tool, MeGASampler. Copyright © 2025, The Authors. All rights reserved.

关键词： software testing

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Correction: CMAE-3D: Contrastive Masked AutoEncoders for Self-Supervised 3D Object Detection(International Journal of computer Vision, 10.1007/s11263-024-02313-2)

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International Journal of computer Vision 2025年第6期133卷 3803-3803页

作者： Zhang, Yanan Chen, Jiaxin Huang, Di State Key Laboratory of Complex and Critical Software Environment Beihang University Beijing100191 China School of Computer Science and Engineering Beihang University Beijing100191 China Hangzhou Innovation Institute Beihang University Hangzhou310051 China

The original version of this article unfortunately contained error in co-author’s affiliation. The first affiliation should be State key laboratory of Complex and Critical software Environment, Beihang University, Beijing 100191, China instead of State key laboratory of Complex and Critical software Environment, School of computer Science and engineering, Beihang University, Beijing 100191, China. Also, the author Yanan Zhang is affiliated with School of computer Science and engineering, Beihang University, Beijing 100191, China. The original article has been corrected. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

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TFSG: A Self-Supervised Guided Graph Learning Method for Traffic Flow Prediction

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IEEE Internet of Things Journal 2025年

作者： Zheng, Yingran Luo, Chao Shao, Rui Shandong Normal University School of Information Science and Engineering Jinan250014 China Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Jinan250014 China China Mobile Shandong Co. Ltd center of network optimization Jinan250014 China

With the rapid development of the Internet of Things, a large amount of real-time traffic data has been collected. How to fully utilize these traffic data for rational planning of intelligent transportation systems is an important issue. Graph learning has proved to be potential tools for modeling complex spatiotemporal relationships in addressing traffic flow prediction problems. Existing methods usually relied on the graph structure constructed from predefined distance information between different regions. However, affected by factors such as road capacity, traffic rules etc., distance bias is non-trivial to affect traffic flow in reality. Besides, the distance based static transportation network structure is also difficult to describe the dynamic and uncertainty of road traffic in complex situations. To address the aforementioned issues, this article proposes an enhanced graph generation approach for traffic flow prediction, which is based on Temporal-Frequency Self-Supervised Guided Graph Learning (TFSG). The method progressively guides the feature latent representation to approach the target latent representation in both the time domain and frequency domain. Meanwhile, a multi-head self-attention mechanism is introduced to capture dependencies among different time series patches, constructing an enhanced graph structure for downstream prediction tasks. Experimental results first independently evaluated the qualities of the generated graphs. Subsequently, by using five public traffic datasets, the feasibility and effectiveness of the proposed method are further verified. © 2014 IEEE.

关键词： Self-supervised learning

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FAMOS: robust privacy-preserving authentication on payment apps via federated multi-modal contrastive learning 24

FAMOS: robust privacy-preserving authentication on payment a...

引用

Proceedings of the 33rd USENIX Conference on Security Symposium

作者： Yifeng Cai Ziqi Zhang Jiaping Gui Bingyan Liu Xiaoke Zhao Ruoyu Li Zhe Li Ding Li Key Laboratory of High Confidence Software Technologies (PKU) Ministry of Education and School of Computer Science Peking University Department of Computer Science University of Illinois Urbana-Champaign School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University School of Computer Science Beijing University of Posts and Telecommunications Ant Group

ISBN: (纸本)9781939133441

The rise of mobile payment apps necessitates robust user authentication to ensure legitimate user access. Traditional methods, like passwords and biometrics, are vulnerable once a device is compromised. To overcome these limitations, modern solutions utilize sensor data to achieve user-agnostic and scalable behavioral authentication. However, existing solutions face two problems when deployed to real-world applications. First, it is not robust to noisy background activities. Second, it faces the risks of privacy leakage as it relies on centralized training with users' sensor *** this paper, we introduce FAMOS, a novel authentication framework based on federated multi-modal contrastive learning. The intuition of FAMOS is to fuse multi-modal sensor data and cluster the representation of one user's data by the action category so that we can eliminate the influence of background noise and guarantee the user's privacy. Furthermore, we incorporate FAMOS with federated learning to enhance performance while protecting users' privacy. We comprehensively evaluate FAMOS using real-world datasets and devices. Experimental results show that FAMOS is efficient and accurate for real-world deployment. FAMOS has an F1-Score of 0.91 and an AUC of 0.97, which are 42.19% and 27.63% higher than the baselines, respectively.

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Climate Downscaling Using Neural Operator: Spatiotemporal Multimodal Fusion Operator with State-Query Coupled Kernel

Climate Downscaling Using Neural Operator: Spatiotemporal Mu...

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

作者： Haodi Zhang Yichi Wang Yifan Jian Jiahui Jiang Zhaohai Bai Lin Ma College of Computer Science and Software Engineering Shenzhen University Center for Agricultural Resources Research Institute of Genetic and Developmental Biology Chinese Academy of Sciences State Key Laboratory of Pollution Control and Resource Reuse School of the Environment Nanjing University

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Climate downscaling is crucial for detailed small- scale analysis and for acquiring climate data in regions without weather stations. Operator learning has proven potential for this task. However, several challenges remain in operator learning, such as multimodal fusion, spatiotemporal fusion and input state and query adaptation. To address these challenges, we propose a Spatiotemporal Multimodal Fusion Operator with a State- Query Coupled Kernel (SMCK). This framework includes a latent space fusion encoder that encodes climate variables using position-wise multihead attention for multimodal fusion and integrates historical information to generate robust and precise representation. Additionally, we introduce a state-query coupled kernel that combines radial basis functions and discrete fourier encoding to enhance query location representation, while also adapting to the state to obtain the coupled kernel. Extensive experiments demonstrate that our method achieves state-of-the-art performance and provides strong support for climate downscaling and the planning of climate-related strategies.

关键词： Couplings Signal processing Encoding Robustness Spatiotemporal phenomena Planning Kernel Speech processing Forecasting Meteorology

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HMGS: Hierarchical Matching Graph Neural Network for Session-Based Recommendation

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computers, Materials and Continua 2025年第3期83卷 5413-5428页

作者： Pengfei Zhang Rui Xin Xing Xu Yuzhen Wang Xiaodong Li Xiao Zhang Meina Song Zhonghong Ou State Grid Hebei Information and Telecommunication Branch Shijiazhuang 050000 China School of Computer Science (National Pilot Software Engineering School) Beijing University of Posts and Telecommunications Beijing 100876 China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing 100876 China

Session-based recommendation systems (SBR) are pivotal in suggesting items by analyzing anonymized sequences of user interactions. Traditional methods, while competent, often fall short in two critical areas: they fail to address potential inter-session item transitions, which are behavioral dependencies that extend beyond individual session boundaries, and they rely on monolithic item aggregation to construct session representations. This approach does not capture the multi-scale and heterogeneous nature of user intent, leading to a decrease in modeling accuracy. To overcome these limitations, a novel approach called HMGS has been introduced. This system incorporates dual graph architectures to enhance the recommendation process. A global transition graph captures latent cross-session item dependencies, while a heterogeneous intra-session graph encodes multi-scale item embeddings through localized feature propagation. Additionally, a multi-tier graph matching mechanism aligns user preference signals across different granularities, significantly improving interest localization accuracy. Empirical validation on benchmark datasets (Tmall and Diginetica) confirms HMGS’s efficacy against state-of-the-art baselines. Quantitative analysis reveals performance gains of 20.54% and 12.63% in Precision@10 on Tmall and Diginetica, respectively. Consistent improvements are observed across auxiliary metrics, with MRR@10, Precision@20, and MRR@20 exhibiting enhancements between 4.00% and 21.36%, underscoring the framework’s robustness in multi-faceted recommendation scenarios.

关键词： Session-based recommendation graph network multi-level matching

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Patient-Level Anatomy Meets Scanning-Level Physics: Personalized Federated Low-Dose CT Denoising Empowered by Large Language Model

arXiv

引用

arXiv 2025年

作者： Yang, Ziyuan Chen, Yingyu Wang, Zhiwen Shan, Hongming Chen, Yang Zhang, Yi College of Computer Science Sichuan University China Key Laboratory of Data Protection and Intelligent Management Ministry of Education Sichuan University China School of Cyber Science and Engineering Sichuan University China School of Computer Science and Software Engineering Southwest Petroleum University China Institute of Science and Technology for Brain-inspired Intelligence Fudan University China School of Computer Science and Engineering Southeast University China

Reducing radiation doses benefits patients, however, the resultant low-dose computed tomography (LDCT) images often suffer from clinically unacceptable noise and artifacts. While deep learning (DL) shows promise in LDCT reconstruction, it requires large-scale data collection from multiple clients, raising privacy concerns. Federated learning (FL) has been introduced to address these privacy concerns;however, current methods are typically tailored to specific scanning protocols, which limits their generalizability and makes them less effective for unseen protocols. To address these issues, we propose SCAN-PhysFed, a novel SCanning- and ANatomy-level personalized Physics-Driven Federated learning paradigm for LDCT reconstruction. Since the noise distribution in LDCT data is closely tied to scanning protocols and anatomical structures being scanned, we design a dual-level physics-informed way to address these challenges. Specifically, we incorporate physical and anatomical prompts into our physics-informed hypernetworks to capture scanning- and anatomy-specific information, enabling dual-level physics-driven personalization of imaging features. These prompts are derived from the scanning protocol and the radiology report generated by a medical large language model (MLLM), respectively. Subsequently, client-specific decoders project these dual-level personalized imaging features back into the image domain. Besides, to tackle the challenge of unseen data, we introduce a novel protocol vector-quantization strategy Copyright © 2025, The Authors. All rights reserved.

关键词： computerized tomography

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Fed RDP: A Robust Federated Learning Framework for Multi Party Decision-Making

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IEEE Transactions on Network and Service Management 2025年

作者： An, Jian Tang, Siyu Ping, Ruyuan Li, Ran He, Xin Jin, Xiaolong Xi’an Jiaotong University School of Computer Science and Technology China Shaanxi Province Key Laboratory of Computer Network Xi’an Jiaotong University Xi’an710049 China Xi’an Jiaotong University School of Computer Science and Technology Xi’an710049 China Henan University School of Software Kaifeng475001 China Rocket Force University of Engineering School of Combat Supporting Xi’an710000 China

In the field of intelligent manufacturing, the vast and heterogeneous nature of data across different departments poses significant challenges for collaborative decision-making. Direct data sharing often leads to severe privacy and security concerns. Although federated learning offers a promising solution, issues such as participant selection and privacy protection remain inadequately addressed. During model training, it is crucial to minimize the influence of low-quality participants and prevent the inference of sensitive information through gradient analysis, which could threaten model performance or data privacy. To address these challenges, this paper proposes a robust federated learning model, Fed-RDP, based on participant contribution evaluation and personalized differential privacy. The model leverages blockchain technology for secure data flow and storage, implemented through smart contracts. Historical parameters are submitted to evaluate contributions based on the concept of the least core, with real-time updates to reputation scores. When participants upload their local models, the scores are used as weights to aggregate and update the global model. Additionally, personalized differential noise is added to the uploaded gradients based on participant scores, preserving privacy while maximizing the utility of the data. Experimental results demonstrate that this approach effectively identifies low-quality participants, optimizes evaluation time, and protects privacy through personalized differential noise. © 2004-2012 IEEE.

关键词： Federated learning

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