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IEEE International Symposium on Biomedical Imaging

作者： Taiping Qu Yangming Yue Qirui Zhang Cheng Wang Zhiqiang Zhang Guangming Lu Wei Du Xiuli Li Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education College of Computer Science and Technology Jilin University Changchun China Deepwise AI Lab Beijing China Jinling Hospital Medical School of Nanjing University Nanjing Jiangsu China

ISBN: (数字)9781538693308

ISBN: (纸本)9781538693315

The potential pattern changes in brain micro-structure can be used for the brain development assessment in children and adolescents by MRI scans. In this paper, we propose a highly accurate and efficient end-to-end brain age estimation network (BAENET) on T1-weighted MRI images. On the network, 3D skipping and outlier constraint loss are designed to accommodate deeper network and increase the robustness. Besides, we incorporate the neuroimaging domain knowledge into stratified sampling for better generalization ability for datasets with different age distributions, and gender learning for more gender-specific features during modeling. We verify the effectiveness of the proposed method on the public ABIDE2 and ADHD200 benchmark, consisting of 382 and 378 normal children scans respectively. Our BAENET achieves MAE of 1.11 and 1.16, significantly outperforming the best reported methods by 5.1 % and 9.4%.

关键词： Three-dimensional displays Brain modeling Estimation Solid modeling Feature extraction Magnetic resonance imaging Task analysis

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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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computational Prediction of Human Body-Fluid Protein

Computational Prediction of Human Body-Fluid Protein

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IEEE International Conference on Bioinformatics and Biomedicine

作者： Dan Shao Xueteng Cui Lan Huang Kai He Yan Wang Yao Wang Key laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education College of Computer Science and Technology Jilin University College of Computer Science and Technology Changchun University Key laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education College of Computer Science and Technology Jilin University

ISBN: (纸本)9781728118680

Research on body fluid proteomes has led to the discoveries of context-dependent proteomics profiles and numerous novel disease biomarkers. Common challenges remain with current proteomics technologies about how to effectively handle the large variety of protein modifications in those fluids. To this end, computational efforts have shown early successes in identifying biomarker proteins in specific human diseases. In this article, we first reviewed published computational methods on this topic and then presented a new database system and a novel deep learning-based method for predicting body-fluid proteins in 16 types of human fluids. The results show that our new system outperforms existing methods and provides a highly promising tool to facilitate clinical proteomic discovery.

关键词： machine learning protein prediction body-fluid proteome deep learning Machine Learning Database systems Artificial Intelligence proteomics protein modifications Fluid Human Body maladjustment Learning-based method Biological Markers Body Fluids

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Design of a Daily Brief Business Report Generator based on Web Scraping with KNN Algorithm 2

Design of a Daily Brief Business Report Generator based on W...

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2nd International Conference on Computer Information Science and Application Technology, CISAT 2019

作者： Lin, Gang Liang, Yanchun Fu, Xiaoyang Chen, Guangshun Cai, Shaocan Zhuhai Laboratory of Key Laboratory of Symbol Computation and Knowledge Engineering Ministry of Education Department of Computer Science and Technology School of Computer Science Zhuhai Guangdong519041 China Department of Computer Science and Technology School of Computer Science Zhuhai College Jilin University Zhuhai Guangdong519041 China

In order to generate a report for an enterprise where there is neither the API supporting from their existing website systems nor the granted database access rights approval, a daily business report generator system based on web scraping with k nearest neighbor (kNN) classification algorithm is proposed in this paper. It covers the web crawler technology that is to access existing website system and extract business data. The kNN algorithm is applied to identify the verification code on the login page, and the brief daily report generating in a spread-sheet style grid. Compared with some OCR engine for image recognition, the system in Python can automatically generate the brief daily business reports by the kNN algorithm, which is better than some library with default training set on validating the verification code. © Published under licence by IOP Publishing Ltd.

关键词： Report generators

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Medical Image Fusion Based on Sparse Representation and Guided Filtering 4

Medical Image Fusion Based on Sparse Representation and Guid...

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2019 4th Annual International Conference on Information System and Artificial Intelligence, ISAI 2019

作者： Huang, Sa Chu, Guangyu Fei, Yifan Zhang, Xiaoli Wang, Hailiang Department of Radiology Second Hospital Jilin University Changchun Jilin130041 China Key Laboratory of Symbol Computation and Knowledge Engineer of Ministry of Education Jilin University Changchun130012 China Department of Neurosurgery Second Hospital Jilin University Changchun Jilin130041 China

In this paper, we proposed a medical image fusion algorithm based on sparse representation and guided filtering. One of attractive features in the algorithm is that it can preserve the structural information of structural image and color information of functional image. We use a sparse representation in low frequency, initialize the dictionary by using DCT transform, and train the dictionary with each input source image as a training example. It not only ensures time complexity, but also ensures that the low-frequency fusion rules are adaptive. At high frequencies, we use the method of guided filtering to extract structural information from high-frequency images, and use the injection method to fuse high-frequency sub-bands to ensure the validity and richness of structural information. Experimental results show that the proposed fusion algorithm is superior to comparative algorithms in terms of subjective and objective evaluation methods. © 2019 Published under licence by IOP Publishing Ltd.

关键词： Image fusion

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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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Explanation Chains Model Based on the Fine-Grained Data 8th

Explanation Chains Model Based on the Fine-Grained Data

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8th CCF International Conference on Natural Language Processing and Chinese Computing, NLPCC 2019

作者： Ma, Fu-Yuan Chen, Wen-Qi Xiao, Min-Hao Wang, Xin Wang, Ying College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbol Computation and Knowledge Engineering Jilin University Ministry of Education Changchun130012 China Changchun Institute of Technology Changchun130012 China College of Software Jilin University Changchun130012 China

ISBN: (纸本)9783030322359

With the development of information society, Recommendation System has been an import tool to help users filter information and create more economic value for enterprises. However, it is difficult for traditional recommendation systems to interpret recommendation results. In order to improve users’ trust in recommendation results, interpretable recommendation models have attracted more and more attention. In this paper, we present the Explanation Chains Model based on the Fine-grained Data (F-ECM) to enhance the effectiveness of recommendation while achieving the interpretability of recommendation. First, we generate parsing trees from user comments and extract three key sentence structure information (i.e., aspects, features and sentiment tendency) from those generated parsing tree. The fine-grained similarity is computed based on the aspects and features of the products to be recommended, and users’ product satisfaction is predicted by combining sentiment tendency. Then the recommendation chain will be constructed according to the satisfaction degree in the recommendation list. Finally, we calculate recommendation chain scores of all the items to be recommended to the target user, generate the recommendation results and personal explanation for the user by the recommendation chains. Experiments in the Amazon data set show that the Explanation Chains Model based on the Fine-grained Data achieve better interpretability and performance of product recommendation systems. © 2019, Springer Nature Switzerland AG.

关键词： Recommender systems

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Automatic localization of macular area based on structure label transfer

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International Journal of Ophthalmology(English edition) 2018年第3期11卷 422-428页

作者： Xiao-Xin Guo Qun Li Chao Sun Yi-Nan LU Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education Changchun130012 Jilin Province China College of Computer Science and Technology Jilin University Changchun 130012 Jilin Province China

AIM： To explore feasibility and practicability of macula localization independent of macular morphological features. METHODS： A novel method was proposed to identify macula in fundus images by using structure label transfer. Its main idea was to match a processed image with the candidate images with known structures, and then transfer the structure label representing the macular to the processed image as a result of macula localization. In this way, macula localization couldn＇t be influenced by lesion or other interference any more. RESULTS： The average success rate in four datasets was 98.18%. For accuracy, the average error distance in four datasets was 0.151 optic disc diameter （ODD）. Even for severe lesion images, the proposed method can still maintain high success rate and high accuracy, e.g., 95.65% and 0.124 ODD in the case of STARE dataset, respectively, which indicated that the proposed method was highly robust and stable in the complicated situations. CONCLUSION： The proposed method can avoid the interference of lesion to macular morphological features in macula localization, and can locate macula with high accuracy and robustness, verifying its feasibility.

关键词： fundus image optic disc macula structurelabel transfer

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RiWalk: Fast Structural Node Embedding via Role Identification

RiWalk: Fast Structural Node Embedding via Role Identificati...

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IEEE International Conference on Data Mining (ICDM)

作者： Xuewei Ma Geng Qin Zhiyang Qiu Mingxin Zheng Zhe Wang College of Computer Science and Technology Jilin University Changchun China Key Laboratory of Symbol Computation and Knowledge Engineering Ministry of Education Jilin University Changchun China

Nodes performing different functions in a network have different roles, and these roles can be gleaned from the structure of the network. Learning latent representations for the roles of nodes helps to understand the network and to transfer knowledge across networks. However, most existing structural embedding approaches suffer from high computation and space cost or rely on heuristic feature engineering. Here we propose RiWalk, a flexible paradigm for learning structural node representations as it decouples the structural embedding problem into a role identification procedure and a network embedding procedure. Through role identification, rooted kernels with structural dependencies kept are built to better integrate network embedding methods. To demonstrate the effectiveness of RiWalk, we develop two different role identification methods named RiWalk-SP and RiWalk-WL respectively and employ random walk based network embedding methods. Experiments on within-network classification tasks show that our proposed algorithms achieve comparable performance with other baselines while being an order of magnitude more efficient. Besides, we also conduct across-network role classification tasks. The results show potential of structural embeddings in transfer learning. RiWalk is also scalable, making it capable of capturing structural roles in massive networks.

关键词： Kernel Task analysis Knowledge engineering Machine learning Bridges Proteins Network topology

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RiWalk: Fast structural node embedding via role identification

arXiv

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arXiv 2019年

作者： Ma, Xuewei Qin, Geng Qiu, Zhiyang Zheng, Mingxin Wang, Zhe College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbol Computation and Knowledge Engineering Ministry of Education Jilin University Changchun130012 China

Nodes performing different functions in a network have different roles, and these roles can be gleaned from the structure of the network. Learning latent representations for the roles of nodes helps to understand the network and to transfer knowledge across networks. However, most existing structural embedding approaches suffer from high computation and space cost or rely on heuristic feature engineering. Here we propose RiWalk, a flexible paradigm for learning structural node representations. It decouples the structural embedding problem into a role identification procedure and a network embedding procedure. Through role identification, rooted kernels with structural dependencies kept are built to better integrate network embedding methods. To demonstrate the effectiveness of RiWalk, we develop two different role identification methods named RiWalk-SP and RiWalk-WL respectively and employ random walk based network embedding methods. Experiments on within-network classification tasks show that our proposed algorithms achieve comparable performance with other baselines while being an order of magnitude more efficient. Besides, we also conduct across-network role classification tasks. The results show potential of structural embeddings in transfer learning. RiWalk is also scalable, making it capable of capturing structural roles in massive networks. Copyright © 2019, The Authors. All rights reserved.

关键词： Network embeddings

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