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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 Novel Locomotion Rule Rmbedding Long Short-Term Memory Network with Attention for Human Locomotor Intent Classification Using Multi-Sensors Signals

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Computers, Materials & Continua 2024年第6期79卷 4349-4370页

作者： Jiajie Shen Yan Wang Dongxu Zhang Key Laboratory of Symbol Computation and Knowledge Engineering Ministry of EducationColleague of Computer Science and TechnologyJilin UniversityChangchun130012China College of Software and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of EducationJilin UniversityChangchun130012China

Locomotor intent classification has become a research hotspot due to its importance to the development of assistive robotics and wearable *** work have achieved impressive performance in classifying steady locomotion ***,it remains challenging for these methods to attain high accuracy when facing transitions between steady locomotion *** to the similarities between the information of the transitions and their adjacent steady ***,most of these methods rely solely on data and overlook the objective laws between physical activities,resulting in lower accuracy,particularly when encountering complex locomotion modes such as *** address the existing deficiencies,we propose the locomotion rule embedding long short-term memory(LSTM)network with Attention(LREAL)for human locomotor intent classification,with a particular focus on transitions,using data from fewer sensors(two inertial measurement units and four goniometers).The LREAL network consists of two levels:One responsible for distinguishing between steady states and transitions,and the other for the accurate identification of locomotor *** classifier in these levels is composed of multiple-LSTM layers and an attention *** introduce real-world motion rules and apply constraints to the network,a prior knowledge was added to the network via a rule-modulating *** method was tested on the ENABL3S dataset,which contains continuous locomotion date for seven steady and twelve transitions *** results showed that the LREAL network could recognize locomotor intents with an average accuracy of 99.03%and 96.52%for the steady and transitions states,*** is worth noting that the LREAL network accuracy for transition-state recognition improved by 0.18%compared to other state-of-the-art network,while using data from fewer sensors.

关键词： Lower-limb prosthetics deep neural networks motion classification

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NADARAYA-WATSON ESTIMATORS FOR REFLECTED STOCHASTIC PROCESSES

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Acta Mathematica Scientia 2024年第1期44卷 143-160页

作者：韩月才张丁文 School of Mathematics Jilin UniversityChangchun130012China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin UniversityChangchun130012China

We study the Nadaraya-Watson estimators for the drift function of two-sided reflected stochastic differential *** estimates,based on either the continuously observed process or the discretely observed process,are *** certain conditions,we prove the strong consistency and the asymptotic normality of the two *** method is also suitable for one-sided reflected stochastic differential *** results demonstrate that the performance of our estimator is superior to that of the estimator proposed by Cholaquidis et al.(Stat Sin,2021,31:29-51).Several real data sets of the currency exchange rate are used to illustrate our proposed methodology.

关键词： reflected stochastic differential equation discretely observed process continuously observed process Nadaraya-Watson estimator asymptotic behavior

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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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Learning continuous network emerging dynamics from scarce observations via data-adaptive stochastic processes

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Science China(Information Sciences) 2024年第12期67卷 240-255页

作者： Jiaxu CUI Qipeng WANG Bingyi SUN Jiming LIU Bo YANG College of Computer Science and Technology Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Public Computer Education and Research Center Jilin University Department of Computer Science Hong Kong Baptist University

Learning network dynamics from the empirical structure and spatio-temporal observation data is crucial to revealing the interaction mechanisms of complex networks in a wide range of domains. However,most existing methods only aim at learning network dynamic behaviors generated by a specific ordinary differential equation instance, resulting in ineffectiveness for new ones, and generally require dense *** observed data, especially from network emerging dynamics, are usually difficult to obtain, which brings trouble to model learning. Therefore, learning accurate network dynamics with sparse, irregularly-sampled,partial, and noisy observations remains a fundamental challenge. We introduce a new concept of the stochastic skeleton and its neural implementation, i.e., neural ODE processes for network dynamics(NDP4ND), a new class of stochastic processes governed by stochastic data-adaptive network dynamics, to overcome the challenge and learn continuous network dynamics from scarce observations. Intensive experiments conducted on various network dynamics in ecological population evolution, phototaxis movement, brain activity, epidemic spreading, and real-world empirical systems, demonstrate that the proposed method has excellent data adaptability and computational efficiency, and can adapt to unseen network emerging dynamics, producing accurate interpolation and extrapolation with reducing the ratio of required observation data to only about 6% and improving the learning speed for new dynamics by three orders of magnitude.

关键词： complex networks network dynamics emerging spatio-temporal dynamics neural processes

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DeciLS-PBO: an effective local search method for pseudo-Boolean optimization

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Frontiers of Computer Science 2024年第5期18卷 239-241页

作者： Luyu JIANG Dantong OUYANG Qi ZHANG Liming ZHANG College of Computer Science and Technology Jilin UniversityChangchun 130012China Key Laboratory of Symbolic Computation and Knowledge Engineering(Ministry of Education) Jilin UniversityChangchun 130012China

1 Introduction Local search method is a rising star for solving combinatorial optimization problems in recent years,and the state-of-the-art local search-based incomplete Maximum Satisfiability(MaxSAT)solversshowpromi... 详细信息

关键词： Max optimization local

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Lightweight axiom pinpointing via replicated driver and customized SAT-solving

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Frontiers of Computer Science 2023年第2期17卷 121-133页

作者： Dantong OUYANG Mengting LIAO Yuxin YE College of Computer Science and Technology Jilin UniversityChangchun 130012China Key Laboratory of Symbolic Computation and Knowledge Engineering(Jilin University) Ministry of EducationChangchun 130012China

In description logic,axiom pinpointing is used to explore defects in ontologies and identify hidden justifications for a logical *** recent years,SAT-based axiom pinpointing techniques,which rely on the enumeration of minimal unsatisfiable subsets(MUSes)of pinpointing formulas,have gained increasing *** with traditional Tableau-based reasoning approaches,SAT-based techniques are more competitive when computing justifications for consequences in large-scale lightweight description logic *** this article,we propose a novel enumeration justification algorithm,working with a replicated *** replicated driver discovers new justifications from the explored justifications through cheap literals resolution,which avoids frequent calls of SAT ***,when the use of SAT solver is inevitable,we adjust the strategies and heuristic parameters of the built-in SAT solver of axiom pinpointing *** adjusted SAT solver is able to improve the checking efficiency of unexplored *** proposed method is implemented as a tool named *** experimental results show that RDMinA outperforms the existing axiom pinpointing tools on practical biomedical ontologies such as Gene,Galen,NCI and Snomed-CT.

关键词： axiom pinpointing description logic SAT solver

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DiagDO: an efficient model based diagnosis approach with multiple observations

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Frontiers of Computer Science 2023年第6期17卷 125-134页

作者： Huisi ZHOU Dantong OUYANG Xinliang TIAN Liming ZHANG Laboratory of Symbolic Computation and Knowledge Engineering Jilin UniversityChangchun 130000China College of Computer Science and Technology Jilin UniversityChangchun 130000China

Model-based diagnosis(MBD)with multiple observations shows its significance in identifying fault *** existing approaches for MBD with multiple observations use observations which is inconsistent with the prediction of the *** this paper,we proposed a novel diagnosis approach,namely,the Diagnosis with Different Observations(DiagDO),to exploit the diagnosis when given a set of pseudo normal observations and a set of abnormal *** ideas are proposed in this ***,for each pseudo normal observation,we propagate the value of system inputs and gain fanin-free edges to shrink the size of possible faulty ***,for each abnormal observation,we utilize filtered nodes to seek surely normal ***,we encode all the surely normal components and parts of dominated components into hard clauses and compute diagnosis using the MaxSAT solver and MCS *** tests on the ISCAS'85 and ITC'99 benchmarks show that our approach performs better than the state-of-the-art algorithms.

关键词： model based diagnosis maximum satisfiability top-level diagnosis cardinality-minimal diagnosis subset-minimal diagnosis

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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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SEOE:an option graph based semantically embedding method for prenatal depression detection

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Frontiers of Computer Science 2024年第6期18卷 245-258页

作者： Xiaosong HAN Mengchen CAO Dong XU Xiaoyue FENG Yanchun LIANG Xiaoduo LANG Renchu GUAN College of Computer Science and Technology Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of EducationJilin UniversityChangchun 130012China Department of Electrical Engineering and Computer Science and Christopher S.Bond Life Sciences Center University of MissouriColumbiaMO 65211USA Department of Computer Science and Technology Zhuhai Laboratory of Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of EducationZhuhai College of Science and TechnologyZhuhai 519041China Jilin Provincial Institute of Population Science and Technology Changchun 130000China

Prenatal depression,which can affect pregnant women’s physical and psychological health and cause postpartum depression,is increasing ***,it is essential to detect prenatal depression early and conduct an attribution *** studies have used questionnaires to screen for prenatal depression,but the existing methods lack *** diagnose the early signs of prenatal depression and identify the key factors that may lead to prenatal depression from questionnaires,we present the semantically enhanced option embedding(SEOE)model to represent questionnaire *** can quantitatively determine the relationship and patterns between options and *** first quantifies options and resorts them,gathering options with little difference,since Word2Vec is highly dependent on *** resort task is transformed into an optimization problem involving the traveling salesman ***,all questionnaire samples are used to train the options’vector using ***,an LSTM and GRU fused model incorporating the cycle learning rate is constructed to detect whether a pregnant woman is suffering from *** verify the model,we compare it with other deep learning and traditional machine learning *** experiment results show that our proposed model can accurately identify pregnant women with depression and reach an F1 score of *** most relevant factors of depression found by SEOE are also verified in the *** addition,our model is of low computational complexity and strong generalization,which can be widely applied to other questionnaire analyses of psychiatric disorders.

关键词： questionnaire analyses text classification graph representation learning traveling salesman problem prenatal depression

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