作者机构：
[Li, Yu; Zhao, Jingjing; Yang, Liping; Zhang, Yong] Cent China Normal Univ, Comp Sch, Wuhan, Hubei, Peoples R China.
会议名称：
International Conference on Big Data and Education (ICBDE)
会议时间：
MAR 30-APR 01, 2019
会议地点：
Univ Greenwich, London, ENGLAND
会议主办单位：
Univ Greenwich
会议论文集名称：
ICBDE'19: Proceedings of the 2019 International Conference on Big Data and Education
关键词：
Knowledge Graph;Entity Relation Extraction;Normalized Google Distance;Intelligent Education;Graph Visualization
摘要：
To make full use of specialized vocabulary in computer science and discover relationships among these words, a Chinese knowledge graph of computer science major is constructed based on the internet web pages, and then the knowledge graph visualization and application for learning guidance based on it are developed. For the construction of computer science knowledge graph, a small amount of important specialized words in computer science are collected manually, and then these words are extended based on Baidu Baike (baike.baidu.com). Thus we get about 3000 specialized words (called entries). The similarity between two entries is calculated based on the Normalized Google Distance (NGD). Once the similarity is greater than a setting value, a link between the two entries is created. Finally the knowledge graph is constructed by these words and links between them. Here the relation type of link is ignored for simplicity. Furthermore the graph visualization is implemented by a tool called sigma.js, and an application for learning guidance is developed by J2EE. Through the application, students can get a visualized overview of computer science major and make a learning plan efficiently. Moreover the application and method of knowledge graph construction can be applied for other majors easily.
作者：
Zhao, Weizhong*;Ma, Huifang;Li, Zhixin;Ao, Xiang;Li, Ning
期刊：
Lecture Notes in Computer Science,2019年11447:555-571 ISSN：0302-9743
通讯作者：
Zhao, Weizhong
作者机构：
[Zhao, Weizhong] Cent China Normal Univ, Sch Comp, Wuhan, Hubei, Peoples R China.;[Zhao, Weizhong] Cent China Normal Univ, Hubei Key Lab Artificial Intelligence & Smart Lea, Wuhan, Hubei, Peoples R China.;[Ma, Huifang] Northwest Normal Univ, Coll Comp Sci & Engn, Lanzhou, Gansu, Peoples R China.;[Li, Zhixin] Guangxi Normal Univ, Guangxi Key Lab Multisource Informat Min & Secur, Guilin, Peoples R China.;[Ao, Xiang] Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing, Peoples R China.
通讯机构：
[Zhao, Weizhong] C;Cent China Normal Univ, Sch Comp, Wuhan, Hubei, Peoples R China.;Cent China Normal Univ, Hubei Key Lab Artificial Intelligence & Smart Lea, Wuhan, Hubei, Peoples R China.
会议名称：
24th International Conference on Database Systems for Advanced Applications (DASFAA)
会议时间：
APR 22-25, 2019
会议地点：
Chiang Mai, THAILAND
会议主办单位：
[Zhao, Weizhong] Cent China Normal Univ, Sch Comp, Wuhan, Hubei, Peoples R China.^[Zhao, Weizhong] Cent China Normal Univ, Hubei Key Lab Artificial Intelligence & Smart Lea, Wuhan, Hubei, Peoples R China.^[Ma, Huifang] Northwest Normal Univ, Coll Comp Sci & Engn, Lanzhou, Gansu, Peoples R China.^[Li, Zhixin] Guangxi Normal Univ, Guangxi Key Lab Multisource Informat Min & Secur, Guilin, Peoples R China.^[Ao, Xiang] Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing, Peoples R China.^[Li, Ning] Chinese Acad Sci, Inst Informat Engn, Beijing, Peoples R China.^[Ao, Xiang] Univ Chinese Acad Sci, Beijing, Peoples R China.
会议论文集名称：
Lecture Notes in Computer Science
关键词：
Social recommendation;Behavior recommendation;Network embedding;Probabilistic matrix factorization
摘要：
Recent years have witnessed the fast growing and ubiquity of social media which has significantly changed the social manner and information sharing in our daily life. Given a user, social (i.e. friend) recommendation and behavior (i.e. item) recommendation are two types of popular services in social media applications. Despite the extensive studies, few existing work has addressed both tasks elegantly and effectively. In this paper, we propose an improved unified framework for Social and Behavior Recommendations with Network Embedding (SBRNE for short). With modeling social and behavior information simultaneously, SBRNE integrates social recommendation and behavior recommendation into a unified framework. By employing users’ latent interests as a bridge, social and behavior information is modeled effectively to improve performance of social and behavior recommendations all together. In addition, an efficient network embedding procedure is introduced as a pre-training step for users’ latent representations to improve effectiveness and efficiency of recommendation tasks. Extensive experiments on real-world datasets demonstrate the effectiveness of SBRNE.
作者机构：
[Jiang, Xingpeng; He, Tingting; Fu, Chengcheng; Li, Xusheng; Zhong, Duo] Cent China Normal Univ, Sch Comp, Wuhan, Hubei, Peoples R China.;[Jiang, Xingpeng; He, Tingting; Fu, Chengcheng; Li, Xusheng; Zhong, Duo] Cent China Normal Univ, Hubei Prov Key Lab Artificial Intelligence & Smar, Wuhan, Hubei, Peoples R China.;[Zhong, Ran] Cent China Normal Univ, Collaborat & Innovat Ctr, Wuhan, Hubei, Peoples R China.
通讯机构：
[Jiang, Xingpeng] C;Cent China Normal Univ, Sch Comp, Wuhan, Hubei, Peoples R China.;Cent China Normal Univ, Hubei Prov Key Lab Artificial Intelligence & Smar, Wuhan, Hubei, Peoples R China.
会议名称：
IEEE International Conference on Bioinformatics and Biomedicine (BIBM) - Bioinformatics and Systems Biology
会议时间：
DEC 03-06, 2018
会议地点：
Madrid, SPAIN
会议主办单位：
[Li, Xusheng;Fu, Chengcheng;Zhong, Duo;He, Tingting;Jiang, Xingpeng] Cent China Normal Univ, Sch Comp, Wuhan, Hubei, Peoples R China.^[Li, Xusheng;Fu, Chengcheng;Zhong, Duo;He, Tingting;Jiang, Xingpeng] Cent China Normal Univ, Hubei Prov Key Lab Artificial Intelligence & Smar, Wuhan, Hubei, Peoples R China.^[Zhong, Ran] Cent China Normal Univ, Collaborat & Innovat Ctr, Wuhan, Hubei, Peoples R China.
关键词：
Named entity recognition;Biomedical text mining;Conditional random field;Deep learning
摘要：
Microorganisms play a vital role in various ecosystems, but their complex interaction is still unclear. With the publication of a large number of microbial literatures, many experimentally verified microbial interaction is dispersed therein. Organizing them into a database or knowledge graph can facilitate the development of microbiology research. Text mining technology is able to automatically extract and integrate these microbial interactions, as well as discover implicit information in literatures. For this purpose, we manually annotate a Microbial Interaction Corpus (MICorpus) containing 1005 abstracts, which provide a useful data source for the MIE task. On this basis, we propose an automated MIE extraction system based on Max-Bi-LSTM model. The best result of the system is precision (P) 76.313%, recall (R) of 90.121%, and an F value (F) 82.476%.
作者机构：
[Ma, Yingjun] Cent China Normal Univ, Sch Math & Stat, Wuhan 430079, Hubei, Peoples R China.;[Jiang, Xingpeng; He, Tingting; Zhang, Chenhao] Cent China Normal Univ, Sch Comp, Wuhan 430079, Hubei, Peoples R China.;[Jiang, Xingpeng; He, Tingting] Cent China Normal Univ, Hubei Prov Key Lab Artificial Intelligence & Smar, Wuhan 430079, Hubei, Peoples R China.;[Ge, Leixin] Cent China Normal Univ, Sch Life Sci, Wuhan 430079, Hubei, Peoples R China.
通讯机构：
[Jiang, Xingpeng] C;Cent China Normal Univ, Sch Comp, Wuhan 430079, Hubei, Peoples R China.;Cent China Normal Univ, Hubei Prov Key Lab Artificial Intelligence & Smar, Wuhan 430079, Hubei, Peoples R China.
会议名称：
IEEE International Conference on Bioinformatics and Biomedicine (BIBM) - Medical Genomics
会议时间：
DEC 03-06, 2018
会议地点：
Madrid, SPAIN
会议主办单位：
[Ma, Yingjun] Cent China Normal Univ, Sch Math & Stat, Wuhan 430079, Hubei, Peoples R China.^[He, Tingting;Zhang, Chenhao;Jiang, Xingpeng] Cent China Normal Univ, Sch Comp, Wuhan 430079, Hubei, Peoples R China.^[He, Tingting;Jiang, Xingpeng] Cent China Normal Univ, Hubei Prov Key Lab Artificial Intelligence & Smar, Wuhan 430079, Hubei, Peoples R China.^[Ge, Leixin] Cent China Normal Univ, Sch Life Sci, Wuhan 430079, Hubei, Peoples R China.
摘要：
Microbial ecosystems are complex, by analyzing co-occurrence modules of microbial communities, we can better understand the conditions of microbial interactions in each environment, and help understand the interaction patterns that maintain the stability of microbial communities. Imbalances in human microbiome are closely related to human disease. Previous modular clustering analysis was based only on the relationship between paired microorganisms. In this paper, we propose calculating the logical relationship between microbial triplet in human body by information entropy and construct a hypergraph based on the triplet network. Based on the hypergraph clustering, we proposed a novel hypergraph clustering algorithm based on intra-class scatter matrix (HCIS) to reconstruct hyperedge similarity, and selected the optimal cluster number by maximizing modularity to analyze higher-order module of microorganisms. The clustering results verify the effectiveness and feasibility of HCIS algorithm for higher-order microbial module analysis.
作者机构：
[Jiang, Xingpeng; He, Tingting; Hu, Xiaohua; Li, Xusheng; Wang, Xiaoyan; Zhong, Duo] Cent China Normal Univ, Sch Comp, Wuhan, Hubei, Peoples R China.;[Hu, Xiaohua] Drexel Univ, Sch Comp & Informat, Philadelphia, PA 19104 USA.;[Zhong, Ran] Cent China Normal Univ, Collaborat & Innovat Ctr, Wuhan, Hubei, Peoples R China.
会议名称：
IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
会议时间：
DEC 03-06, 2018
会议地点：
Madrid, SPAIN
会议主办单位：
[Li, Xusheng;Wang, Xiaoyan;Zhong, Duo;He, Tingting;Hu, Xiaohua;Jiang, Xingpeng] Cent China Normal Univ, Sch Comp, Wuhan, Hubei, Peoples R China.^[Hu, Xiaohua] Drexel Univ, Sch Comp & Informat, Philadelphia, PA 19104 USA.^[Zhong, Ran] Cent China Normal Univ, Collaborat & Innovat Ctr, Wuhan, Hubei, Peoples R China.
会议论文集名称：
IEEE International Conference on Bioinformatics and Biomedicine-BIBM
关键词：
biomedical text mining;bacterial named entity recognition;conditional random field;deep learning;microbial interaction
摘要：
Microorganisms have been confirmed to be essential for the fundamental function of various ecosystems. The interactions among microorganisms affect the human health and environmental ecosystem. A large number of microbial interactions with experimental confidence have been reported in biomedical literature. Extracting and collating these interactions with experimental confidence into a database will create a valuable data resource. Named Entity Recognition (NER) is the premise and key to interaction extraction from literatures. Especially, bacterial named entity recognition is still a challenging task due to the specialty of bacterial names. In this paper, we propose a bacterial named entity recognition system based on a hybrid deep learning framework (HDL-CRF), which integrates two deep learning models: the bidirectional long short-term memory network and the convolutional neural network, as well as the conditional random field approach, for automatically extracting the features. Finally, we prove that this model outperforms previous methods in performance.
作者：
Jian, Fanghong;Huang, Jimmy Xiangji*;Zhao, Jiashu;He, Tingting（何婷婷）
作者机构：
[Huang, Jimmy Xiangji] Cent China Normal Univ, Informat Retrieval & Knowledge Management Res Lab, Wuhan, Hubei, Peoples R China.;Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Hubei, Peoples R China.;Cent China Normal Univ, Sch Comp, Wuhan, Hubei, Peoples R China.;York Univ, Sch Informat Technol, Toronto, ON, Canada.
会议名称：
41st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR)
会议时间：
JUL 08-12, 2018
会议地点：
Univ Michigan, Ann Arbor, MI
会议主办单位：
Univ Michigan
关键词：
Term Frequency Normalization;BM25;Probabilistic Model
摘要：
In- probabilistic BM25, term frequency normalization is one of the key components. It is often controlled by parameters k(1) and b, which need to be optimized for each given data set. In this paper, we assume and show empirically that term frequency normalization should be specific with query length in order to optimize retrieval performance. Following this intuition, we first propose a new term frequency normalization with query length for probabilistic information retrieval, namely BM25(QL). Then BM25(QL) is incorporated into the state-of-the-art models CRTER2 and LDA-BM25, denoted as CRTER2QL and LDA-BM25(QL) respectively. A series of experiments show that our proposed approaches BM25(QL), CRTER2QL and LDA-BM25(QL) are comparable to BM25, CRTER2 and LDA-BM25 with the optimal b setting in terms of MAP on all the data sets.
摘要：
One category of neural information retrieval models tries to learn text representation in a common embedding space for both queries and documents. However, a single embedding space is not always sufficient, since queries and documents are different in terms of length, number of topics covered, etc. We argue that queries and documents should be mapped into different but overlapping embedding spaces, which is named Partially Shared Embedding Space (PSES) model in this paper. PSES consists of two embedding spaces respectively for queries and documents, and a shared embedding space capturing common features of two sources. Those three embeddings are learned by jointly obeying three constraints: a feature separation constraint, a pairwise matching constraint, and a reconstruction constraint. Experiments on standard TREC collections indicate that PSES leads to significant better performance of retrieval over traditional IR models and several neural IR models with only one embedding space.
会议名称：
14th International Conference on Information Security Practice and Experience (ISPEC)
会议时间：
SEP 25-27, 2018
会议地点：
Tokyo, JAPAN
会议主办单位：
[Liu, Yi-Ning;Wang, Yan-Ping] Guilin Univ Elect Technol, Guangxi Key Lab Trusted Software, Guilin, Peoples R China.^[Wang, Xiao-Fen] Univ Elect Sci & Technol China, Sch Comp Sci & Engn, Chengdu, Sichuan, Peoples R China.^[Xia, Zhe] Wuhan Univ Technol, Sch Comp Sci, Wuhan, Hubei, Peoples R China.^[Xu, Jingfang] Cent China Normal Univ, Sch Comp, Wuhan, Hubei, Peoples R China.
会议论文集名称：
Lecture Notes in Computer Science
关键词：
Privacy-preservation;Sensing tasks;Anonymity
摘要：
Lack of reliable data is a major obstacle in some research works because users are unwilling to provide their own private data to any third parties directly. Since statistical inference is aimed to analyze the overall data of a well-defined group rather than a specific individual, the paradigm of privacy-preserving data collection scheme is proposed recently, which can motivate users to contribute their data to research works. In this paper, two probable properties that promote the success of sensing tasks are analyzed, and a fog-assisted data collection scheme for mobile phone sensing tasks is proposed. Sensitive measurements are particularly protected by obfuscating them with the group values, which not only provides anonymity for participants but also enables accurate data for the task provider. Especially, the dynamic change of participants is also considered. Theoretical analysis shows that this method achieves the desired security goals, and experiments are performed to demonstrate the efficiency and feasibility.
摘要：
Virus-host association studies are significant for understanding the complex functions and dynamics of microbial communities of human health or diseases. Several virus-host association prediction methods have been developed based on the information of sequences, virus networks, host networks and virus-host networks separately. In this study, we develop a heterogeneous network approach based on neighborhood regularization logistic matrix factorization (LMFH-VH) which integrate the virus similarity network and the host similarity network using known virus-host associations. The virus similarity network and the host similarity network were constructed based on oligonucleotide frequency measures and Gaussian interaction profile kernel similarity, respectively. LMFH-VH achieves the best performance on several validation datasets comparing with other four network-based methods. The host prediction accuracy of LMFH-VH is 24.17% and 12.8% higher than two recently proposed virus-host prediction methods, respectively. The codes and datasets are available at https://github.com/liudan111/LMFH-VH.git.
作者机构：
[Cai, Xiaohong; Ma, Li; Wang, Yan] Hubei Univ Chinese Med, Informat Engn Coll, Wuhan, Hubei, Peoples R China.;[Liu, Yunfang] Hubei Univ Econ, Law & Business Coll, Wuhan, Hubei, Peoples R China.;[Yu, Lihui] Cent China Normal Univ, Sch Comp, Wuhan, Hubei, Peoples R China.;[Shen, Shaowu] Hubei Univ Chinese Med, Informat Engn Coll, Inst Standardizat & Informat Technol, Wuhan, Hubei, Peoples R China.
会议名称：
IEEE International Conference on Bioinformatics and Biomedicine (BIBM) - Human Genomics
会议时间：
DEC 03-06, 2018
会议地点：
Madrid, SPAIN
会议主办单位：
[Wang, Yan;Ma, Li;Cai, Xiaohong] Hubei Univ Chinese Med, Informat Engn Coll, Wuhan, Hubei, Peoples R China.^[Liu, Yunfang] Hubei Univ Econ, Law & Business Coll, Wuhan, Hubei, Peoples R China.^[Yu, Lihui] Cent China Normal Univ, Sch Comp, Wuhan, Hubei, Peoples R China.^[Shen, Shaowu] Hubei Univ Chinese Med, Informat Engn Coll, Inst Standardizat & Informat Technol, Wuhan, Hubei, Peoples R China.
会议论文集名称：
IEEE International Conference on Bioinformatics and Biomedicine-BIBM
关键词：
Logic relationships;Famous Veteran Teran Doctors of TCM;insomnia;regularity;Standardization
摘要：
Data mining is a technology that combines traditional data analysis methods with complex algorithms that process large amounts of data. Under the guidance of data mining technology, this study discussed the law of prescriptions of famous old Chinese medicine from the steps of data pretreatment, standardization, data analysis and so forth. Logic relationships method is used to decipher correlation from Chinese herb. In the end, we took the medical case of TCM clinical treatment of insomnia as an example, the case is analyzed through data collation, logical association analysis and data mining analysis, it explained the specific application of the aforementioned strategies and methods.
期刊：
Lecture Notes in Computer Science,2018年10955:93-99 ISSN：0302-9743
通讯作者：
Zhang, Yue
作者机构：
[Pan, Min] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Hubei, Peoples R China.;[Jiang, Xingpeng; He, Tingting; Zhang, Yue] Cent China Normal Univ, Sch Comp Sci, Wuhan 430079, Hubei, Peoples R China.
通讯机构：
[Zhang, Yue] C;Cent China Normal Univ, Sch Comp Sci, Wuhan 430079, Hubei, Peoples R China.
会议名称：
14th International Conference on Intelligent Computing (ICIC)
会议时间：
AUG 15-18, 2018
会议地点：
Wuhan, PEOPLES R CHINA
会议主办单位：
[Pan, Min] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Hubei, Peoples R China.^[Zhang, Yue;He, Tingting;Jiang, Xingpeng] Cent China Normal Univ, Sch Comp Sci, Wuhan 430079, Hubei, Peoples R China.
摘要：
In an actual electronic health record (EHR), patient notes are written with terse language and clinical jargons. However, most Pseudo Relevance Feedback (PRF) technique methods do not take into account the significant degree of candidate term in feedback documents and the co-occurrence relationship between a candidate term and a query term simultaneously. In this paper, we study how to incorporate proximity information into the Rocchio's model, and propose a HAL-based Rocchio's model, called HRoc. A new concept of term proximity feedback weight is introduced to model in the query expansion. Then, we propose three normalization methods to incorporate proximity information. Experimental results on 2016 TREC Clinical Support Medicine collections show that our proposed models are effective and generally superior to the state-of-the-art relevance feedback models.
作者机构：
[Ma, Yingjun] Cent China Normal Univ, Sch Math & Stat, Wuhan, Hubei, Peoples R China.;[Hu, Xiaohua; Yu, Limin; He, Tingting; Jiang, Xingpeng] Cent China Normal Univ, Sch Comp, Wuhan, Hubei, Peoples R China.
会议名称：
IEEE International Conference on Bioinformatics and Biomedicine (BIBM) - Human Genomics
会议时间：
DEC 03-06, 2018
会议地点：
Madrid, SPAIN
会议主办单位：
[Ma, Yingjun] Cent China Normal Univ, Sch Math & Stat, Wuhan, Hubei, Peoples R China.^[Yu, Limin;He, Tingting;Hu, Xiaohua;Jiang, Xingpeng] Cent China Normal Univ, Sch Comp, Wuhan, Hubei, Peoples R China.
会议论文集名称：
IEEE International Conference on Bioinformatics and Biomedicine-BIBM
摘要：
Long non-coding RNA (1ncRNA) has a close relationship with multiple biological processes and complex diseases. Generally speaking, it functions through the interaction with corresponding RNA-binding proteins. However, it is costly and time-consuming to use experimental methods to detect IncRNA-protein interactions. Network-based prediction methods have been developed recently, but very few methods consider the integration of multiple features and the non-linear relationship of IncRNAs (proteins). In this paper, we propose a kernel-based soft-neighborhood propagation algorithm (LKSNS) to predict the potential 1ncRNA-protein interactions. The method not only makes use of the non-neighborhood information, but also excavates the potential non-linear relationship. We compare LKSNS with other state-of-the-art methods based on multiple datasets and the results show that LKSNS has significantly better prediction performance. The case study further demonstrates that the LKSNS has the good practicality for 1ncRNA-protein interaction prediction.
摘要：
Aiming at the problem of "learning defiance" and "information overload" brought by educating big data to learners, this paper proposes an online learning community personalized learning path recommendation algorithm based on ant colony algorithm: in terms of computing pheromone, it combines individuality. Based on the characteristics of the learning path, a learning path scoring method based on multi-factor fuzzy evaluation is proposed to quantify the learning path evaluation as a score to solve the problem that it is difficult for the subjective score to accurately represent the pheromone concentration; in terms of pheromone updating rules, The introduction of pheromone restriction intervals avoids the problems associated with excessive or small learning path pheromone concentration in global updating; in the calculation of the selection probability of local search, the positive and negative feedback effects of pheromones can be better used. Search for a local optimal solution. The related experiments show that this algorithm can effectively solve the recommendation of the personalized learning path of the online learning community.
通讯机构：
[Xie, Wei] C;Cent China Normal Univ, Coll Comp Sci, Wuhan 430079, Hubei, Peoples R China.
会议名称：
10th International Conference on Digital Image Processing (ICDIP)
会议时间：
MAY 11-14, 2018
会议地点：
E China Normal Univ, Shanghai, PEOPLES R CHINA
会议主办单位：
E China Normal Univ
会议论文集名称：
Proceedings of SPIE
关键词：
gradient field;guidance filter;light intensity compensation;fusion;auto white balance
摘要：
In order to avoid the problem that the color distortion and the details are not obvious, this paper presents an improved defogging algorithm. It uses the dark channel prior to estimate the atmospheric light and the transmission and proposes a new gradient domain filtering to refine the transmission. Then, the intensity compensation is used to enhance the image color information. At the same time, the atmospheric light value is estimated and boundary constrains is used to roughly estimate the transmission, and, weights is added to define the transmission. So the scene transmission of both is refined, the image is reduced by atmospheric scattering model and two images are fused with the fusion strategy. The fusion of image uses the auto white balance to fine tune to get the final image. According to the experimental results, the algorithm effectively solves the problem of color distortion and loss of detail information in the defog image.
会议名称：
IEEE Congress on Evolutionary Computation (IEEE CEC) as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI)
会议时间：
JUL 08-13, 2018
会议地点：
Rio de Janeiro, BRAZIL
会议主办单位：
[Jiang, Shouyong;Kaiser, Marcus;Krasnogor, Natalio] Newcastle Univ, Sch Comp, Newcastle Upon Tyne NE4 5TG, Tyne & Wear, England.^[Wan, Shuzhen] China Three Gorges Univ, Sch Comp Sci & Informat Technol, Yichang, Peoples R China.^[Guo, Jinglei] Cent China Normal Univ, Dept Comp Sci, Wuhan, Hubei, Peoples R China.^[Yang, Shengxiang] De Montfort Univ, Sch Comp Sci & Informat, Leicester, Leics, England.
会议论文集名称：
IEEE Congress on Evolutionary Computation
摘要：
Dynamic multiobjective optimisation deals with multiobjective problems whose objective functions, search spaces, or constraints are time-varying during the optimisation process. Due to wide presence in real-world applications, dynamic mul-tiobjective problems (DMOPs) have been increasingly studied in recent years. Whilst most studies concentrated on DMOPs with only two objectives, there is little work on more objectives. This paper presents an empirical investigation of evolutionary algorithms for three-objective dynamic problems. Experimental studies show that all the evolutionary algorithms tested in this paper encounter performance degradedness to some extent. Amongst these algorithms, the multipopulation based change handling mechanism is generally more robust for a larger number of objectives, but has difficulty in deal with time-varying deceptive characteristics.
作者机构：
[Jiang, Xingpeng; Yang, Jincai; He, Tingting; Shen, Xianjun; Hu, Xiaohua; Shen, XJ; Gong, Xue] Cent China Normal Univ, Sch Comp, Wuhan, Hubei, Peoples R China.;[Hu, Xiaohua] Drexel Univ, Coll Comp & Informat, Philadelphia, PA 19104 USA.
会议名称：
IEEE International Conference on Bioinformatics and Biomedicine (BIBM) - Human Genomics
会议时间：
DEC 03-06, 2018
会议地点：
Madrid, SPAIN
会议主办单位：
[Shen, Xianjun;Gong, Xue;Jiang, Xingpeng;Yang, Jincai;He, Tingting;Hu, Xiaohua] Cent China Normal Univ, Sch Comp, Wuhan, Hubei, Peoples R China.^[Hu, Xiaohua] Drexel Univ, Coll Comp & Informat, Philadelphia, PA 19104 USA.
会议论文集名称：
IEEE International Conference on Bioinformatics and Biomedicine-BIBM
关键词：
weighted Directed motifs;microbial network;high order structures;motif-based clustering
摘要：
High-order connectivity patterns are essential to understanding the basic structure of complex networks. Network motifs are considered as the basic building blocks of complex networks. From identifying network motifs to discovering higher-order modular organizations by them, it is helpful to study the organization principles and functional modules of the biological networks in a divide-and-conquer manner. However, the current research based on network motifs often neglect the influence of weight in network motifs. In this paper, the concept of weighted motifs was presented and was applied to microbial network. The method was proposed to find the optimal weighted motif in microbial network and analyze the high-order structure of weighted networks based on them. It also proved that the partially weighted motifs can obtain optimal clusters in theory over unweighted ones.
摘要：
In the online learning environment, identifying learners' behaviors in the learning process can help them improve their learning effect autonomously. Firstly, we use K-Means algorithm to cluster the learner's help-seeking behavior data to get the classification label of the learner's help-seeking behavior. Secondly, we use the t-distributed Stochastic Neighbor Embedding(T-sne) algorithm to reduce the dimension of the data to visualize the clustering result. Finally, the learner's help-seeking behavior data and the help-seeking behavior classification labels are used as training data to train the Naive Bayesian model so as to automatically obtain the help-seeking behavior classification for the data generated by the new learner. Via the analysis and processing of the help-seeking behavior data using the method proposed in this paper, it shows that this method can effectively find online learners' help-seeking behavior classifications.
作者机构：
[Zhu, Qiang] Cent China Normal Univ, Sch Informat Management, Wuhan 430079, Hubei, Peoples R China.;[Jiang, Xingpeng; He, Tingting; Hu, Xiaohua; Pan, Min; Zhu, Qing] Cent China Normal Univ, Sch Comp, Wuhan 430079, Hubei, Peoples R China.;[Hu, Xiaohua] Drexel Univ, Coll Comp & Informat, Philadelphia, PA 19104 USA.
通讯机构：
[He, Tingting] C;Cent China Normal Univ, Sch Comp, Wuhan 430079, Hubei, Peoples R China.
会议名称：
IEEE International Conference on Bioinformatics and Biomedicine (BIBM) - Human Genomics
会议时间：
DEC 03-06, 2018
会议地点：
Madrid, SPAIN
会议主办单位：
[Zhu, Qiang] Cent China Normal Univ, Sch Informat Management, Wuhan 430079, Hubei, Peoples R China.^[Zhu, Qing;Pan, Min;Jiang, Xingpeng;Hu, Xiaohua;He, Tingting] Cent China Normal Univ, Sch Comp, Wuhan 430079, Hubei, Peoples R China.^[Hu, Xiaohua] Drexel Univ, Coll Comp & Informat, Philadelphia, PA 19104 USA.
会议论文集名称：
IEEE International Conference on Bioinformatics and Biomedicine-BIBM
摘要：
Microorganisms are closely related to human health and have an impact on the development of various diseases. It is extremely significant to identify the relationships between microorganisms and the phenotypes (such as healthy or disease status) by analyzing microbial abundance in personalized medicine. Deep learning allows computational models that composed of multiple processing layers to learn representation of data with multiple levels of abstraction. These methods have improved the state-of-the-art in speech recognition, visual object recognition and object detection. However, current deep models are typically neural networks which are actually multiple layers of parameterized differentiable nonlinear models that can be trained by backpropagation. It is interesting to explore other deep learning models to handle tasks with small sample size and high dimensional data. While a unique feature of microbial data is that it has phylogenetic tree structure information which can be embedded to improve the classification performance. In this work, in order to further improve the metagenomic classification, we propose a deep model named Cascade Deep Forest which keeps the spatial structure between nodes through embedding phylogenetic tree information. Our results demonstrate: 1) the modified cascade structure can enhance the classification performance of Deep Forest; 2) embedding phylogenetic tree information can also improve the classification of the models; 3) Deep Forest achieves highly competitive performance to deep neural networks.
