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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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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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Object Attribute Matters in Visual Question Answering

arXiv

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arXiv 2023年

作者： Li, Peize Si, Qingyi Fu, Peng Lin, Zheng Wang, Yan School of Artificial Intelligence Jilin University Changchun China Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education College of Computer Science and Technology Jilin University Changchun China

Visual question answering is a multimodal task that requires the joint comprehension of visual and textual information. However, integrating visual and textual semantics solely through attention layers is insufficient to comprehensively understand and align information from both modalities. Intuitively, object attributes can naturally serve as a bridge to unify them, which has been overlooked in previous research. In this paper, we propose a novel VQA approach from the perspective of utilizing object attribute, aiming to achieve better object-level visual-language alignment and multimodal scene understanding. Specifically, we design an attribute fusion module and a contrastive knowledge distillation module. The attribute fusion module constructs a multimodal graph neural network to fuse attributes and visual features through message passing. The enhanced object-level visual features contribute to solving fine-grained problem like counting-question. The better object-level visual-language alignment aids in understanding multimodal scenes, thereby improving the model’s robustness. Furthermore, to augment scene understanding and the out-of-distribution performance, the contrastive knowledge distillation module introduces a series of implicit knowledge. We distill knowledge into attributes through contrastive loss, which further strengthens the representation learning of attribute features and facilitates visual-linguistic alignment. Intensive experiments on six datasets, COCO-QA, VQAv2, VQA-CPv2, VQA-CPv1, VQAvs and TDIUC, show the superiority of the proposed method. © 2023, CC BY.

关键词： Distillation

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Intensity preserving technology in image restoration

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Jilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (engineering and Technology Edition) 2013年第SUPPL.1期43卷 106-109页

作者： Guo, Xiao-Xin Xu, Zhi-Wen Che, Xiang-Jiu 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 new approach of image restoration for the sequence of fluorescein angiography was proposed. First, the intensity constraint was incorporated into Miller regularization equation to obtain the ability to preserve the high intensity of pixels. Thus, the restored image would be influenced not only by the smoothness constraints, but also by the intensity constraints. Secondly, in the process of the restoration, we use an intensity template obtained from the pre-filtering procedure was used to achieve the intensity constraints. The template represented an image composed by the desired intensity value. The experiments show that the proposed scheme gains a better result in both high intensity preservation and image restoration.

关键词： Image reconstruction

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Research on collaborative filtering recommendation based on k-means clustering

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ICIC Express Letters 2016年第10期10卷 2493-2498页

作者： Dong, Liyan Zhu, Gaozi Zhu, Qi Li, Yongli College of Computer Science and Technology Jilin University No. 2699 Qianjin Street Changchun130012 China Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education Jilin University No. 2699 Qianjin Street Changchun130012 China School of Computer Science and Information Technology Northeast Normal University No. 2555 Jingyue Ave Changchun130117 China

In accordance with the inaccuracy of searching neighbors in traditional collaborative filtering algorithms, we narrow down the space of neighbor searching by means of partition clustering to improve the real-time performance of recommendations. Then by taking the similarity measurement into consideration, we convert the high-dimensional sparse matrix of user rating to the low-dimensional dense matrix of user interest to enhance the accuracy of recommendations. Experiments proved that combining the partition clustering based on user interests and collaborative filters could contribute to increasing real-time speed and the accuracy of recommendation effectively. © 2016 ICIC International.

关键词： Collaborative filtering

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A multi-level neural network for implicit causality detection in Web texts

arXiv

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arXiv 2019年

作者： Liang, Shining Zuo, Wanli Shi, Zhenkun Wang, Sen Wang, Junhu Zuo, Xianglin Key Laboratory of Symbol Computation Knowledge Engineering of Ministry of Education Changchun China College of Computer Science and Technology Jilin University Jilin China Tianjin Institute of Industrial Biotechnology Chinese Academy of Sciences School of Information Technology and Electrical Engineering The University of Queensland Brisbane Australia School of Information and Communication Technology Griffith University QLD Australia

Mining causality from text is a complex and crucial natural language understanding task corresponding to the human cognition. Existing studies at its solution can be grouped into two primary categories: feature engineering based and neural model based methods. In this paper, we find that the former has incomplete coverage and inherent errors but provide prior knowledge;while the latter leverages context information but causal inference of which is insufficiency. To handle the limitations, we propose a novel causality detection model named MCDN to explicitly model causal reasoning process, and furthermore, to exploit the advantages of both methods. Specifically, we adopt multi-head self-attention to acquire semantic feature at word level and develop the SCRN to infer causality at segment level. To the best of our knowledge, with regards to the causality tasks, this is the first time that the Relation Network is applied. The experimental results show that: i) the proposed approach performs prominent performance on causality detection;ii) further analysis manifests the effectiveness and robustness of MCDN. © 2019, CC BY-NC-SA.

关键词： Semantics

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A new qualitative spatial reasoning model for natural language understanding

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ICIC Express Letters, Part B: Applications 2011年第5期2卷 1015-1020页

作者： Song, Xiaohua Ouyang, Dantong College of Computer Science and Technology Jilin University No. 2699 Qianjin Street Changchun 130012 China Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry Education Jilin University No. 2699 Qianjin Street Changchun 130012 China Center of Mathematics for Applications Aviation University of Air Force No. 7855 Renmin Street Changchun 130022 China

This paper introduces a qualitative spatial model based on previously developed models for representation and reasoning the spatial information described in natural language. The model is integrated with direction and topology which make it more useful than CDC and ICD to describe complex spatial information in natural language. Then, we give the reasoning method and discuss its advantage. © 2011 ISSN 2185-2766.

关键词： Topology

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Design of vehicle automatic braking systems considering drivers' braking characteristics

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International Journal of Performability engineering 2019年第9期15卷 2338-2345页

作者： Gao, Zhenhai Sun, Tianjun Hassan, MuHammad Wang, Liupu College of Automotive Engineering Jilin University Changchun130022 China 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 Department of Computer Science and Information Technology University of Malakand KP Pakistan

With the rapid development of advanced driver assistance systems (ADAS), an automatic braking system has become increasingly important when faced with complicated traffic. The conventional decision-making method for the braking system is focused on security but lacks consideration of drivers' characteristics, which generates feelings of fear and frustration. We propose an anthropomorphic braking method based on vehicle kinematic characteristics and dynamic theories to improve the original system and eliminate the sense of jerking. Furthermore, on the basis of the traditional time to collision (TTC) algorithm, we consider the random motion of the target vehicle and develop a dynamic time to collision (DTTC) algorithm to meet different drivers' braking requirements. Therefore, in this study, the braking model is optimized by a series of driving simulator and real-vehicle braking tests. Finally, the validity and feasibility of the method are verified through the simulations of different braking modes. © 2019 Totem Publisher, Inc. All rights reserved.

关键词： Advanced driver assistance systems

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Multi-Label Classification based on Label-Specific Features

Multi-Label Classification based on Label-Specific Features

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2011年全国理论计算机科学学术年会

作者： QU Huaqiao ZHANG Shichao LIU Huawen ZHAO Jianmin Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun 130012 China Department of Computer Science Zhejiang Normal University Jinhua 321004 Department of Computer Science Zhejiang Normal University Jinhua 321004 China Department of Computer Science Zhejiang Normal University Jinhua 321004 China

lti-label learning aims at predicting a proper label set for each unseen *** instance in the dataset is associated with a set of predefined ***-label learning approaches frequently used choose identical feature set to...

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