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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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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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SIoTFog:Byzantine-resilient IoT fog networking

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Frontiers of Information Technology & Electronic engineering 2018年第12期19卷 1546-1557页

作者： Jian-wen XU Kaoru OTA Mian-xiong DONG An-feng LIU Qiang LI Department of Information and Electronic Engineering Muroran Institute of Technology School of Information Science and Engineering Central South University MOE Key Laboratory of Symbol Computation and Knowledge Engineering Jilin University

The current boom in the Internet of Things(IoT) is changing daily life in many ways, from wearable devices to connected vehicles and smart cities. We used to regard fog computing as an extension of cloud computing, but it is now becoming an ideal solution to transmit and process large-scale geo-distributed big data. We propose a Byzantine fault-tolerant networking method and two resource allocation strategies for IoT fog computing. We aim to build a secure fog network, called "SIoTFog," to tolerate the Byzantine faults and improve the efficiency of transmitting and processing IoT big data. We consider two cases, with a single Byzantine fault and with multiple faults, to compare the performances when facing different degrees of risk. We choose latency, number of forwarding hops in the transmission, and device use rates as the metrics. The simulation results show that our methods help achieve an efficient and reliable fog network.

关键词： Byzantine fault tolerance Fog computing Resource allocation Internet of Things(IoT)

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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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Modern deep learning in bioinformatics

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Journal of Molecular Cell Biology 2020年第11期12卷 823-827页

作者： Haoyang Li Shuye Tian Yu Li Qiming Fang Renbo Tan Yijie Pan Chao Huang Ying Xu Xin Gao Cancer Systems Biology Center The China-Japan Union HospitalJilin UniversityChangchun 130033China MOE Key Laboratory of Symbolic Computation and Knowledge Engineering College of Computer Science and TechnologyJilin UniversityChangchun 130012China Department of Biology Southern University of Science and TechnologyShenzhen 518055China Computational Bioscience Research Center(CBRC) Computer Electrical and Mathematical Sciences and Engineering(CEMSE)DivisionKing Abdullah University of Science and Technology(KAUST)Thuwal 23955Saudi Arabia School of Computer Science and Technology Hangzhou Dianzi UniversityHangzhou 310018China Ningbo Institute of Information Technology Application Chinese Academy of SciencesNingbo 315040China Computational Systems Biology Lab Department of Biochemistry and Molecular Biology and Institute of BioinformaticsUniversity of GeorgiaAthensGA 30602USA

Deep learning(DL)has shown explosive growth in its application to bioinformatics and has demonstrated thrillingly promising power to mine the complex relationship hidden in large-scale biological and biomedical data.A number of comprehensive reviews have been published on such applications,ranging from high-level reviews with future perspectives to those mainly serving as tutorials.

关键词： has such she

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

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

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

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 *** covers the web crawler technology that is to access existing website system and extract business *** k NN algorithm is applied to identify the verification code on the login page,and the brief daily report generating in a spreadsheet style *** 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.

关键词： image Design of a Daily Brief Business Report Generator based on Web Scraping with KNN Algorithm Web KNN

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An effective dimensionality reduction method for text classification based on TFP-tree

An effective dimensionality reduction method for text classi...

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作者： Liu, Lu College of Software Jilin University Changchun China Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education Changchun China College of Computer Science and Technology Jilin University Changchun China

Obtaining interesting and topic-relevant information is a very important task in Web mining. Text classification using a small proportion of labeled data and a large proportion of unlabeled data, also called semi-supervised learning, is a well-known problem. Despite plenty of research on text classification, however, how to effectively and efficiently apply valuable frequent patterns and deal with high-dimensional data in text classification is still an open issue. Due to the increasing data volumes and plenty of high-dimensional data, both distance measures and time complexity could be influenced by the noisy data. This paper targets on this problem and presents a novel method for text classification called CTFP (Classification based on TFP-tree), which uses TFP-tree (Text-Frequent-Pattern-tree) to generate frequent patterns in tremendous amount of texts and conduct text classification in a relatively low dimensional data space. It effectively reduces the data dimensionality during constructing the classifier. Substantial experiments on three datasets (RCV1, SRAA and Reuters-21578) show that our proposed method can achieve better performance than many existing state-of-the-art methods on precision, efficiency and many other evaluation metrics. © 2018 - IOS Press and the authors. All rights reserved.

关键词： Support vector machines

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