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International Conference on Electrical engineering, Computing Science and Automatic Control (CCE)

作者： Xuchu Dong Yonggang Zhang Dianbo Cai Haihong Yu Yuxin Ye Department of Computer Science and Technology Jilin University Changchun Jilin China Key Laboratory of Symbol Computation and Knowledge Engineering Ministry of Education Changchun Jilin China Network Monitor Maintenance Centre China Telecom Corporation Limited Changchun Jilin China

To find an optimal elimination ordering for Bayesian networks, a multi-heuristic-based ant colony system named MHC-HS-ACS is proposed. MHC-HS-ACS uses a set of heuristics to guide the ants to search solutions. The heuristic set can evolve with the searching procedure in an adaptive way. MHC-HS-ACS also utilizes a heuristic-based local search to accelerate its convergence. computational experiments show that MHC-HS-ACS can find very high quality solutions.

关键词： Bayesian methods Ethics Junctions Conferences Electrical engineering IEEE catalog Inference algorithms

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Cooperative coevolutionary genetic algorithms to find optimal elimination orderings for Bayesian networks

Cooperative coevolutionary genetic algorithms to find optima...

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International Conference on Bio-Inspired Computing: Theories and Applications, BICTA

作者： Xuchu Dong HaiHong Yu Dantong Ouyang Dianbo Cai Yuxin Ye Yonggang Zhang Department of Computer Science and Technology Jilin University Changchun Jilin China Key Laboratory of Symbol Computation and Knowledge Engineering Ministry of Education Changchun Jilin China Network Monitor Maintenance Centre China Telecom Corporation Limited Changchun Jilin China

According to the characteristics of the optimal elimination ordering problem in Bayesian networks, a heuristic-based genetic algorithm, a cooperative coevolutionary genetic framework and five grouping schemes are proposed. Based on these works, six cooperative coevolutionary genetic algorithms are constructed. Numerical experiments show that these algorithms are more robust than other existing swarm intelligence methods when solving the elimination ordering problem.

关键词： Genetics Bayesian methods Robustness

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MI-CAT: A Transformer-Based Domain Adaptation Network for Motor

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SSRN 2023年

作者： Zhang, Dongxue Li, Huiying Xie, Jingmeng Jilin University College of Computer Science and Technology Jilin Province Changchun China Key Laboratory of Symbol Computation and Knowledge Engineering Jilin University Changchun130012 China Xi’an Jiaotong University College of Electronic information Shanxi Province Xi’an China

Due to its convenience and safety, electroencephalography (EEG) data is one of the most widely used signals in motor imagery (MI) brain-computer interfaces (BCIs). In recent years, methods based on deep learning have been widely applied to the field of BCIs, and some studies have gradually tried to apply Transformer to EEG signal decoding due to its superior global information focusing ability. However, EEG signals vary from subject to subject. Based on Transformer, how to effectively use data from other subjects (source domain) to improve the classification performance of a single subject (target domain) remains a challenge. To fill this gap, we propose a novel architecture called MI-CAT. The architecture innovatively utilizes Transformer's self-attention and cross-attention mechanisms to interact features to resolve differential distribution between different domains. Specifically, we adopt a patch embedding layer for the extracted source and target features to divide the features into multiple patches. Then, we comprehensively focus on the intra-domain and inter-domain features by stacked multiple Cross-Transformer Blocks (CTBs), which can adaptively conduct bidirectional knowledge transfer and information exchange between domains. Furthermore, we also utilize two non-shared domain-based attention blocks to efficiently capture domain-dependent information, optimizing the features extracted from the source and target domains to assist in feature alignment. To evaluate our method, we conduct extensive experiments on two real public EEG datasets, Dataset IIb and Dataset IIa, achieving competitive performance with an average classification accuracy of 85.26% and 76.81%, respectively. Experimental results demonstrate that our method is a powerful model for decoding EEG signals and facilitates the development of the Transformer for brain-computer interfaces (BCIs). © 2023, The Authors. All rights reserved.

关键词： Classification (of information)

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A hybrid discrete PSO-SA algorithm to find optimal elimination orderings for Bayesian networks

A hybrid discrete PSO-SA algorithm to find optimal eliminati...

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International Conference on Industrial and Information Systems, IIS

作者： Xuchu Dong Dantong Ouyang Dianbo Cai Yonggang Zhang Yuxin Ye Department of Computer Science and Technology Jilin University Changchun Jilin China Key Laboratory of Symbol Computation and Knowledge Engineering Ministry of Education Changchun Jilin China Network Monitor Maintenance Centre China Telecom Corporation Limited Changchun Jilin China

In this paper, a hybrid algorithm named DPSOSA is proposed to find near-to-optimal elimination orderings in Bayesian networks. DPSO-SA is a discrete particle swarm optimization method enhanced by simulated annealing. ... 详细信息

关键词： computational modeling Adaptation model

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A novel multi-task learning network for skin lesion classification based on multi-modal clues and label-level fusion

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Computers in Biology and Medicine 2024年 175卷 108549-108549页

作者： Lin, Qifeng Guo, Xiaoxin Feng, Bo Guo, Juntong Ni, Shuang Dong, Hongliang Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education Jilin University 2699 Qianjin Street Changchun130012 China College of Computer Science and Technology Jilin University 2699 Qianjin Street Changchun130012 China College of Software Jilin University 2699 Qianjin Street Changchun130012 China

In this paper, we propose a multi-task learning (MTL) network based on the label-level fusion of metadata and hand-crafted features by unsupervised clustering to generate new clustering labels as an optimization goal. We propose a MTL module (MTLM) that incorporates an attention mechanism to enable the model to learn more integrated, variable information. We propose a dynamic strategy to adjust the loss weights of different tasks, and trade off the contributions of multiple branches. Instead of feature-level fusion, we propose label-level fusion and combine the results of our proposed MTLM with the results of the image classification network to achieve better lesion prediction on multiple dermatological datasets. We verify the effectiveness of the proposed model by quantitative and qualitative measures. The MTL network using multi-modal clues and label-level fusion can yield the significant performance improvement for skin lesion classification. © 2024 Elsevier Ltd

关键词： Metadata

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Event Prediction Model Combining Ordinary Differential Equation and Hypernetworks

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SSRN 2023年

作者： Zhang, Junhe Ma, Fuyuan Wang, Xin Yang, Bo Wang, Ying College of Computer Science and Technology Jilin University Jilin Changchun130012 China College of Artificial Intelligence Jilin University Jilin Changchun130012 China Key Laboratory of Symbol Computation and Knowledge Engineering Jilin University Ministry of Education Jilin Changchun130012 China

Modeling the temporal evolution patterns of network events is crucial for enhancing user experience and creating commercial value. Existing methods combine graph neural networks and time-series models to capture spatial and temporal features, but they assume non-interaction among nodes when the network structure is stable, implying that node states do not undergo updates. Moreover, utilizing a single model to fit all events in these methods may lead to an excessive emphasis on global evolutionary patterns, potentially neglecting the individual heterogeneity among events. In this paper, we propose an event prediction model combining Ordinary Differential Equations and Hypernetworks, named ODEH. Firstly, to model the continuous evolution process of network structures under stability, we utilize ordinary differential equations based on graph neural networks to capture the continuous information dissemination among nodes and the interaction between node representation channels. Then, we use a message passing mechanism to model the direct impact of network topology changes on network entities. Finally, a hypernetwork is used to capture the individual characteristics of events and fine-tune the prediction model. Experimental results on multiple datasets demonstrate ODEH's superiority over baseline methods, highlighting its effectiveness in event prediction. © 2023, The Authors. All rights reserved.

关键词： Graph neural networks

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Translating Query for Deep Web Using Ontology

Translating Query for Deep Web Using Ontology

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International Conference on Computer Science and Software engineering

作者： Hao Liang Wanli Zuo Fei Ren Junhua Wang College of Computer Science and Technology Department of Technology Jilin University Changchun Taxation China College of Computer Science and Technology Key Laboratory of Symbol Computation andKnowledge Engineering of the Ministry of Education Jilin University China College of Computer Science and Technology Jilin University Changchun China College of Computer Science and Technology Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Changchun China

ISBN: (纸本)9781424439027

The e-commerce information on the surface Web is supported by the Deep Web, which can not be accessed directly by the search engines or the Web crawlers. The only way to access the backend database is through query interface. Extracting valid attributes from the query forms and automatic translating the source query into a target query is a solvable way for addressing the current limitations in accessing Deep Web data sources. To generate a valid query, we have to reconcile the key attributes and their semantic relation. We present our framework to solve the problem. To enrich the set of attributes contained in the semantic form, we use the WordNet as kinds of ontology technique and we try to find the semantic relation of the attributes in the same query from and different forms. Extensive experiments over real-word domains show the utility of our query translation framework.

关键词： Ontologies Databases Computer science Educational institutions Search engines Data mining Laboratories knowledge engineering Computer science education Crawlers

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Self-organization based divide and conquer algorithm for distributed constraint optimization problems

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Jisuanji Yanjiu yu Fazhan/Computer Research and Development 2008年第11期45卷 1831-1839页

作者： Huang, Jing Liu, Dayou Yang, Bo Jin, Di College of Computer Science and Technology Jilin University Changchun 130012 China Key Laboratory of Symbol Computation and Knowledge Engineering Jilin University Changchun 130012 China

Distributed constraint optimization problem (DCOP) is a kind of optimization problem oriented to large-scale, open and dynamic network environments, which has been widely applied in many fields such as computational grid, multimedia networks, e-business, enterprise resource planning and so on. Besides the features such as non-linear and constraint-satisfaction which the traditional optimization problems have, DCOP has its distinct features including dynamic evolution, regional information, localized control and asynchronous updating of network states. It has become a challenge for computer scientists to find out a large-scale, parallel and intelligent solution for DCOP. So far, there have been a lot of methods for solving this problem. However, most of them are not completely decentralized and require prior knowledge such as the global structures of networks as their inputs. Unfortunately, for many applications the assumption that the global views of networks can not be obtained beforehand is not true due to their huge sizes, geographical distributions or decentralized controls. To solve this problem, a self-organizing behavior based divide and conquer algorithm is presented, in which multiple autonomous agents distributed in networks work together to solve the DCOP through a proposed self-organization mechanism. Compared with existing methods, this algorithm is completely decentralized and demonstrates good performance in both efficiency and effectiveness.

关键词： Geographical distribution

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An improved genetic algorithm to optimize vehicle scheduling for relief efforts

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International Journal of Performability engineering 2019年第9期15卷 2356-2363页

作者： Zhou, You Sun, Lecheng Zhou, Xu Parmar, Milan Wang, Liupu College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbol Computation Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China Cancer Hospital Attached Xinjiang Medical University Urumqi830000 China Faculty of Engineering and Computing Coventry University CoventryCV1 5FB United Kingdom

The vehicle scheduling problem is a typical multi-objective optimization problem. It is a core logistics dispatching problem and the key to realize system optimization. Optimizing vehicle scheduling for relief efforts has important practical significance and can be applied to a broader range of applications. Compared with other vehicle scheduling problems, vehicle optimal scheduling for relief efforts is mostly used to perform spot delivery or security tasks. In this paper, we propose an improved genetic algorithm and apply it to solve the vehicle scheduling problem for relief efforts in the equipment technical area. The experimental results demonstrate the effectiveness and robustness of our proposed algorithm. © 2019 Totem Publisher, Inc. All rights reserved.

关键词： Genetic algorithms

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Text Categorization Method Based on Improved Mutual Information and Characteristic Weights Evaluation Algorithms

Text Categorization Method Based on Improved Mutual Informat...

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International Conference on Fuzzy Systems and knowledge Discovery (FSKD)

作者： Zhili Pei Xiaohu Shi Maurizio Marchese Yanchun Liang College of Computer Science and Technology Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education Jilin University Changchun China College of Mathematics & Computer Science National University of Inner Mongolia Tongliao China Department of Information and Communication Technology University of Trento Povo TN USA

The improvement of text categorization by statistical methods can be performed from two main directions, namely the feature selection and the evaluation of characteristic weights. In this paper, we propose an enhanced text categorization method based on a modified mutual information algorithm and evaluation algorithm of characteristic weights which improves both aspects. The proposed method is applied to the benchmark test set Reuters-21578 Top10 to examine its effectiveness. Numerical results show that the precision, the recall and the value of F1 of the proposed method are all superior to those of existing conventional methods.

关键词： Text categorization Mutual information Frequency shift keying Frequency estimation Computer science Statistical analysis Testing Educational institutions Communications technology Performance evaluation

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