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Bayesian Network Reconstruction and Iterative Divergence Problem Solving Method Based on Norm Minimization

Computer Modeling in engineering & Sciences 2025年第4期143卷 617-637页

作者： Kuo Li Aimin Wang Limin Wang Yuetan Zhao Xinyu Zhu Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin UniversityChangchun130012China College of Software Jilin UniversityChangchun130012China

A Bayesian network reconstruction method based on norm minimization is proposed to address the sparsity and iterative divergence issues in network reconstruction caused by noise and missing *** method achieves precise adjustment of the network structure by constructing a preliminary random network model and introducing small-world network characteristics and combines L1 norm minimization regularization techniques to control model complexity and optimize the inference process of variable *** the experiment of game network reconstruction,when the success rate of the L1 norm minimization model’s existence connection reconstruction reaches 100%,the minimum data required is about 40%,while the minimum data required for a sparse Bayesian learning network is about 45%.In terms of operational efficiency,the running time for minimizing the L1 normis basically maintained at 1.0 s,while the success rate of connection reconstruction increases significantly with an increase in data volume,reaching a maximum of 13.2 ***,in the case of a signal-to-noise ratio of 10 dB,the L1 model achieves a 100% success rate in the reconstruction of existing connections,while the sparse Bayesian network had the highest success rate of 90% in the reconstruction of non-existent *** the analysis of actual cases,the maximum lift and drop track of the research method is 0.08 *** mean square error is 5.74 cm^(2).The results indicate that this norm minimization-based method has good performance in data efficiency and model stability,effectively reducing the impact of outliers on the reconstruction results to more accurately reflect the actual situation.

关键词： Bayesian norm minimization network reconstruction iterative divergence sparsity

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A level set model with shape prior constraint for intervertebral disc MRI image segmentation

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Multimedia Tools and Applications 2025年第9期84卷 6755-6783页

作者： Tian, Zhuangzhou Wang, Shengsheng 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

Accurate intervertebral disc image segmentation is necessary for further treatment. However, existing methods are difficult to segment due to the intensity inhomogeneity of intervertebral disc MRI images and the similarity in intensity between the intervertebral disc and the surrounding regions. This paper proposes a novel level set model by introducing a shape prior constraint for automatic and accurate segmentation. We utilize a detection model and intensity change to obtain rectangle prior information, which is used to define a shape constraint term. Then, combining data and regularizing terms construct an energy function and employ the steepest descent method minimization to get segmentation results. Finally, we apply the proposed new method to segment intervertebral disc images and get promising results. Extensive experiments and comparisons show that our model is more efficient and accurate. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Image segmentation

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Auxiliary Tasks Benefit Skeleton-based Action Recognition

Auxiliary Tasks Benefit Skeleton-based Action Recognition

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Yang, Yuheng Chen, Haipeng College of Computer Science and Technology Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Changchun China

ISBN: (纸本)9798350368741

Skeleton-based action recognition has long been a fundamental and intriguing problem in machine intelligence. This task is challenging due to pose occlusion and rapid motion, which typically results in incomplete or noisy skeleton data. State-of-the-art methods tend to learn human motion directly from these corrupted skeletons as if they were reliable. Unfortunately, this might lead to unsatisfactory results when key regions of the skeleton are occluded or disturbed. To tackle the problem, we propose a novel framework that integrates auxiliary tasks into a motion modeling network. These auxiliary tasks corrupt partial human skeletons with masking or noise and then force the network to recover the corrupted data, explicitly facilitating robust feature representation learning. We further propose supervising the auxiliary tasks with mutual information losses, mathematically ensuring feature consistency and spatial alignment between the recovered and original skeleton data. Empirically, our approach sets the new state-of-the-art performance on three benchmark datasets. © 2025 IEEE.

关键词： auxiliary task mutual information skeleton-based action recognition

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PBCounter:weighted model counting on pseudo-boolean formulas

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Frontiers of Computer Science 2025年第3期19卷 55-63页

作者： Yong LAI Zhenghang XU Minghao YIN College of Computer Science and Technology Jilin UniversityChangchun 130012China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin UniversityChangchun 130012China School of Computer Science and Information Technology Northeast Normal UniversityChangchun 130017China

In Weighted Model Counting(WMC),we assign weights to literals and compute the sum of the weights of the models of a given propositional formula where the weight of an assignment is the product of the weights of its *** current WMC solvers work on Conjunctive Normal Form(CNF)***,CNF is not a natural representation for human-being in many *** by the stronger expressive power of Pseudo-Boolean(PB)formulas than CNF,we propose to perform WMC on PB *** on a recent dynamic programming algorithm framework called ADDMC for WMC,we implement a weighted PB counting tool *** compare PBCounter with the state-of-the-art weighted model counters SharpSAT-TD,ExactMC,D4,and ADDMC,where the latter tools work on CNF with encoding methods that convert PB constraints into a CNF *** experiments on three domains of benchmarks show that PBCounter is superior to the model counters on CNF formulas.

关键词： weighted model counting pseudo-boolean constraint algebraic decision diagrams preprocessing techniques

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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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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

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

ISBN: (纸本)9798350368741

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. © 2025 IEEE.

关键词： hyperspectral image Mamba spectral super-resolution state space model

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Transformer-based multi-level attention integration network for video saliency prediction

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Multimedia Tools and Applications 2025年第13期84卷 11833-11854页

作者： Tan, Rui Sun, Minghui Liang, Yanhua Software College Jilin University Qianjin Street Jilin Changchun130012 China College of Computer Science and Technology Jilin University Qianjin Street Jilin Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Qianjin Street Jilin Changchun130012 China

Most existing models for video saliency prediction heavily rely on 3D convolutional operations to extract spatio-temporal features. However, it is worth noting that 3D convolution produces a local receptive field, which may struggle to capture long-range spatio-temporal dependencies effectively. To compensate for such shortage, this paper introduces a novel approach called the Transformer-based Multi-level Attention Integration Network (TMAI-Net) for video saliency prediction. TMAI-Net is designed as a two-stream encoder-decoder model, carefully integrating multi-level features of semantic information. Our model incorporates a Multi-level Interactive Attention(MLIA) module and a Transformer, both implemented based on self-attention mechanism, which are placed at different levels of the model to capture long-range spatio-temporal feature dependencies. Additionally, our model operates on input video frames and attentional patches, allowing the Transformer module to capture structural similarities between related objects in global features and attention features. This, in turn, enables the model to allocate increased attention to salient areas. The efficacy of our proposed approach is validated through extensive experiments conducted on three widely recognized benchmark datasets. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Forecasting

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ETFIDS: An Entropy-Driven, Time-Frequency Analysis Framework for In-Vehicle CAN Signal Intrusion Detection

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IEEE Internet of Things Journal 2025年

作者： Liu, Wanning Qin, Guihe Liang, Yanhua Song, Jiaru Liu, Qingxin Zhou, Xue Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education College of Computer Science and Technology Changchun130012 China

In recent years, cyberattacks against automobiles have exposed significant security threats to in-vehicle networks. The vulnerability of communication signals to malicious interference and manipulation can lead to serious system failures or abnormal behavior. The existing in-vehicle network intrusion detection methods do not fully exploit the time-frequency characteristics of Controller Area Network (CAN) signals. This limitation reduces their effectiveness in capturing subtle changes and signal complexity. Based on the above motivation, from the perspective of signal perception, we propose an entropy-driven, time-frequency analysis framework for in-vehicle network intrusion detection. The framework integrates a signal sampler, a frequency-domain detector, and a time-domain detector. The signal sampler, as the system's front-end module, extracts real-time physical signal data streams from CAN messages. The frequency-domain detector identifies frequency components, detecting high-frequency disturbances and cyclic variations. Meanwhile, the time-domain detector captures instantaneous changes and sudden anomalies. It analyzes signal complexity and anomalies through both stream and block detection. Experimental results demonstrate that the proposed method performs well under various attack scenarios, offering superior detection and real-time performance. It effectively senses multiple signal anomalies, providing a robust intrusion detection solution for modern in-vehicle networks. © 2025 IEEE.

关键词： Network intrusion

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An Area Optimization Approach for Large-Scale RM-TB Dual Logic Circuits Based on a Multitasking Optimization Algorithm

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IEEE Transactions on Computers 2025年

作者： Wu, Xiaoqian Wang, Peng Li, Shaoquan Liu, Huaxiao Liu, Lei Jilin University College of Computer Science and Technology Changchun China Key Laboratory of Symbolic Computation China Knowledge Engineering of Ministry of Education China

Logic synthesis is a crucial step in integrated circuit design, and area optimization is an indispensable part of this process. However, the area optimization problem for large-scale Fixed Polarity Reed-Muller (FPRM) circuits is an NP-hard problem. To address this problem, we divide Boolean circuits into small-scale circuits based on the idea of divide-and-conquer using the proposed grouping decomposition mechanism. Each small-scale Boolean circuit is transformed into an FPRM circuit by a polarity transformation algorithm. To ensure the circuit’s functionality remains unaffected, we integrate FPRM circuits into an FPRM and Boolean (RM-TB) dual logic circuit based on the proposed gate-level integration. However, the area optimization problem of RM-TB dual logic circuits is a multi-task, high-dimensional, and multi-extremal combinatorial optimization problem. Therefore, we propose a Multipopulation Multitasking Optimization Algorithm (MMuOA) that integrates self-evolution with a multitasking equilibrium optimizer and cross-task evolution through knowledge sharing and transfer. This forms a dynamic optimization framework for simultaneously searching for the optimal polarity corresponding to the minimal area of RM-TB dual logic circuits. Moreover, we propose an Area Optimization Approach (AOA) for an RM-TB dual logic circuit with the minimum area using the MMuOA. Experimental results based on the Microelectronics Center of North Carolina (MCNC) Benchmark test circuits demonstrate the effectiveness and superiority of the AOA compared to the state-of-the-art area optimization approach. © 1968-2012 IEEE.

关键词： Combinatorial optimization

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Dual Space Representation Learning for Skeleton-based Action Recognition

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IEEE Signal Processing Letters 2025年 32卷 2104-2108页

作者： Yang, Yuheng Chen, Haipeng Liu, Zhenguang Hu, Sihao Jiao, Yingying Jilin University College of Computer Science and Technology Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Changchun130012 China Zhejiang University School of Cyber Science and Technology Hangzhou310007 China Georgia Institute of Technology College of Computing GA United States

Skeleton-based action recognition is crucial for machine intelligence. Current methods generally learn from 3D articulated motion sequences in the straightforward Euclidean space. Yet, the vanilla Euclidean space may not be the optimal choice for modeling the intricate correlations among human body joints. This challenge arises from the non-Euclidean nature of human anatomy, where joint correlations often vary non-linearly during movement. To address this, we propose a dual space representation learning method. Specifically, we represent the motion sequences in Hyperbolic space, leveraging its intrinsic properties to capture the non-Euclidean latent anatomy of human motions. We then incorporate the motion features from both Hyperbolic and Euclidean spaces, allowing us to precisely model the non-linear joint correlations while effectively sketching human poses. The proposed method empirically achieves state-of-the-art performance on the NTU RGB+D 60, NTURGB+D 120, and NW-UCLA datasets. © 1994-2012 IEEE.

关键词： Digital arithmetic

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Dual-level Mixup for Graph Few-shot Learning with Fewer Tasks 25

Dual-level Mixup for Graph Few-shot Learning with Fewer Task...

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34th ACM Web Conference, WWW 2025

作者： Liu, Yonghao Li, Mengyu Giunchiglia, Fausto Huang, Lan Li, Ximing Feng, Xiaoyue Guan, Renchu College of Computer Science and Technology Jilin University Changchun China Department of Information Engineering and Computer Science University of Trento Trento Italy Key Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education China

ISBN: (纸本)9798400712746

Graph neural networks have been demonstrated as a powerful paradigm for effectively learning graph-structured data on the web and mining content from it. Current leading graph models require a large number of labeled samples for training, which unavoidably leads to overfitting in few-shot scenarios. Recent research has sought to alleviate this issue by simultaneously leveraging graph learning and meta-learning paradigms. However, these graph meta-learning models assume the availability of numerous meta-training tasks to learn transferable meta-knowledge. Such assumption may not be feasible in the real world due to the difficulty of constructing tasks and the substantial costs involved. Therefore, we propose a SiMple yet effectIve approach for graph few-shot Learning with fEwer tasks, named SMILE. We introduce a dual-level mixup strategy, encompassing both within-task and across-task mixup, to simultaneously enrich the available nodes and tasks in meta-learning. Moreover, we explicitly leverage the prior information provided by the node degrees in the graph to encode expressive node representations. Theoretically, we demonstrate that SMILE can enhance the model generalization ability. Empirically, SMILE consistently outperforms other competitive models by a large margin across all evaluated datasets with in-domain and cross-domain settings. Our anonymous code can be found here. © 2025 Copyright held by the owner/author(s).

关键词： Graph neural networks

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