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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

SSRN

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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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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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TDCA: Improved Optimization Algorithm with Degree Distribution and Communication Traffic for the Deployment of Software Components Base on AUTOSAR Architecture

Research Square

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Research Square 2021年

作者： Zhang, Kunpeng Liu, Yanheng Zhang, Jindong Zhang, Guanhua Jin, Jingyi Li, Yunhao Tang, Fengmin College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbol Computation and Knowledge Engineering The Ministry of Education Jilin University Changchun China State Key Laboratory of Automobile Simulation and Control Jilin University Changchun China College of Mechanical Engineering Hebei University of Technology Tianjin300132 China China Automotive Technology and Research Center Tianjin300300 China

AUTOSAR (Automotive Open System Architecture), as an open, standardized framework for automotive electronic software development, has gradually become the standard followed by major automotive manufacturers and automotive electronic device suppliers. The electronic software system problem improves the development efficiency and portability of the system by reducing the development cost of automotive electronic software while ensuring the quality of products and services, which is beneficial for subsequent upgrades and updates of the system. In order to improve the reliability of the software component deployment algorithm based on AUTOSAR architecture, we proposed the TDCA algorithm. During the execution of the algorithm, communication volume and communication degree are introduced to improve the accuracy of the deployment plan by optimizing the bus load and ECU balancing. Algorithm comparison experiments show that comparing heuristic and linear optimization algorithms, the TDCA algorithm proposed in this paper has significant advantages in reducing bus load and ECU utilization. The algorithm can reduce the communication between cores and balance ECU load according to the constraints of AUTOSAR architecture. © 2021, CC BY.

关键词： Equalizers

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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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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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Identification of Cancer Development Related Pathways Based on Co-Expression Analyses

Identification of Cancer Development Related Pathways Based ...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Hongyang Jiang Zhihang Wang Chaoyi Yin Peishuo Sun Ying Xu Huiyan Sun School of Artificial Intelligence Jilin University Changchun Innovation Group of Marine Engineering Materials and Corrosion Control Southern Marine Science and Engineering Guangdong Laboratory Zhuhai China College of Computer Science and Technology Jilin University Changchun China School of Artificial Intelligence Jilin University Changchun China Computational Systems Biology Lab Institute of Bioinformatics University of Georgia Athens USA School of Artificial Intelligence Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun

ISBN: (数字)9781728162157

ISBN: (纸本)9781728162164

Cancer is a rapidly evolving disease, with complex physiological changes throughout its development. Different patients of the same cancer type may require distinct treatments depending on the level of development. Hence it is essential to identify the genes and biological processes strongly associated with cancer progression to design an effective treatment plan. Our study has found that cancer samples of the same development stage (or grade, subtype) tend to share highly unique co-expression patterns, providing considerably stronger discerning power than differential expressions for cancer staging (and grading, subtyping). Based on this, we have developed a framework for identification and analyses of genes and pathways strongly associated with a cancer's development through identification of co-expression patterns that become increasingly stronger or weaker over cancer samples from early through advanced stages. Functional analyses of such co-expressed genes reveal that (1) cell-cycle, immune response, ribosome, proteasome and oxidative phosphorylation, among others, strongly associate with cancer development, (2) the co-expression patterns among ribosome, proteasome and oxidative phosphorylation genes tend to become increasingly weaker as a cancer advances, for almost all cancer types, and (3) the co-expression patterns among cell cycle and immune response genes tend to become increasingly stronger with cancer progression. We anticipate that co-expression-based analyses like we present here will become a key technique for functional studies of cancer development and evolution.

关键词： Cancer Correlation Indexes Biological processes Physiology Immune system Genetic expression

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Consistency label-activated region generating network for weakly supervised medical image segmentation

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Computers in Biology and Medicine 2024年 173卷 108380-108380页

作者： Du, Wei Huo, Yongkang Zhou, Rixin Sun, Yu Tang, Shiyi Zhao, Xuan Li, Ying Li, Gaoyang Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education College of Computer Science and Technology Jilin University Changchun130012 China Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine Shanghai East Hospital Shanghai200092 China Bioinformatics Department School of Life Sciences and Technology Tongji University Shanghai200092 China

The current methods of auto-segmenting medical images are limited due to insufficient and ambiguous pathonmorphological labeling. In clinical practice, rough classification labels (such as disease or normal) are more commonly used than precise segmentation masks. However, there is still much to be explored regarding utilizing these weak clinical labels to accurately determine the lesion mask and guide medical image segmentation. In this paper, we proposed a weakly supervised medical image segmentation model to directly generate the lesion mask through a class activation map (CAM) guided cycle-consistency label-activated region transferring network. Cycle-consistency enforces that the mappings between the two domains should be reversible, which ensures that the original image can be reconstructed from the translated image. We developed a complementary branches fusion module to address the issue of blurry boundaries in CAM-guided segmentation. The complementary branch preserves the original semantic information of the non-lesion region and perfectly fuses the transferred feature of the lesion region with a complementary mask-constrained fake image generation process to clear the boundary of the lesion and non-lesion regions. This module allows the class transformation to focus solely on the label-activated region, resulting in more explicit segmentation. This model can accurately identify different region of medical images at the pixel-level while preserving the overall semantic structure semantion. It organizes disease labels and corresponding regions during image synthesis. Our method utilizes a joint discrimination strategy that significantly enhances the precision of the produced lesion mask. Extensive experiments of the proposed method on BraTs, ISIC and COVID-19 datasets demonstrate superior performance over existing state-of-the-art methods. The code and datasets are available at: https://***/mlcb-jlu/MedImgSeg. © 2024 Elsevier Ltd

关键词： Image segmentation

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Optical flow computation based on spline functionals

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Jilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (engineering and Technology Edition) 2013年第SUPPL.1期43卷 165-168页

作者： Guo, Xiao-Xin Yang, Guang Xu, Zhi-Wen College of Computer Science and Technology Jilin University Changchun 130012 China Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education Jilin University Changchun 130012 China

A mathematical model using the spline functional as smooth constraints was presented. The second-and fourth-order partial differential equations constraints were two special cases in the model. The necessary condition for optical flow minimization problem solution was also presented. This model provided a basis for formal representation and numerical computation of optical flow from a methodological point of view. The significance of this mathematical model lay in the simplification of the equations for optical flow computation into linear algebraic equations. The simplification can contribute to discrete representation of the optical flow equation, and also verify that the use of smoothness constraints can ensure the existence and uniqueness of the solution from the view of the algebraic equations.

关键词： Optical flows

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