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5th International Conference on Geo-Spatial Knowledge and Intelligence, GSKI 2017

作者： Zheng, Yifeng Li, Guohe Zhang, Wenjie School of Computer Sciences Key Laboratory of Data Science and Intelligence Application Fujian Province University Minnan Normal University Zhangzhou China College of Geophysics and Information Engineering Beijing Key Lab of Data Mining for Petroleum Data University China University of Petroleum Beijing China

ISBN: (纸本)9789811308956

Quality and quantity are the two key factors to influence the accuracy of classification. In order to improve the classification accuracy, in this paper, we propose a new algorithm, called CMCM (Classification based on Multiple Classifier Models), which consists of two classification models. In Model1, we mainly focus on the improvement of quality, thus the best attribute value from both the items and their complements in the training set is selected as the first item of a classification rule. While in Model2, quantity is taken into consideration, so it constructs two candidate sets and uses the one-versus-many strategy to generate several rules at one time. The experiment results show that: (1) Model1 can extract sufficient high quality rules and achieve high classification accuracy. (2) Model2 can extract sufficient information and achieve high classification accuracy. (3) CMCM can achieve higher classification accuracy compare with traditional classification. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Classification (of information)

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Optimal strategy routing in leo satellite network based on cooperative game theory 2nd

Optimal strategy routing in leo satellite network based on c...

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2nd International Conference on Space Information Networks, SINC 2017

作者： Wei, Songjie Cheng, Hao Liu, Meilin Ren, Milin School of Computer Science and Engineering Nanjing University of Science and Technology 200 Xiaolingwei Nanjing210094 China Shanghai Institute of Satellite Engineering Shanghai Academy of Spaceflight Technology 3666 Yuanjiang Rd Shanghai201109 China Beijing Engineering Research Center of NGI & Its Major Application Technologies Co. Ltd. 1 East Zhongguancun Rd Beijing100084 China

ISBN: (纸本)9789811078767

To overcome the network difficulty such as long propagation delay and traffic load imbalance when forwarding data packets through multiple hops in LEO satellite network, we propose an optimal strategy based routing algorithm for LEO satellite network using the cooperative game theory. With the idea of cooperation for mutual benefit, a LEO satellite node has the motivation of improving its own network performance gain by cooperation with neighbors to accomplish the multi-hop forwarding task of data packets. A satellite node compares the benefits of joining various routing coalitions, and tends to choose a more powerful next-hop coalition member node for data packet forwarding, which effectively distributes and balances the traffic load of the popular nodes. Such preference also improves the utilization of the satellite network resources, and reduces the average network delay of data packets. Our simulation results show that compared with the traditional satellite routing algorithms based on the shortest path, the average packet routing delay using the proposed algorithm is reduced by 18.5% and the traffic load balance of the network is increased by 65.6%. By replacing the shortest routing path with a path of satellite nodes with the expected optimal routing performance benefit, the proposed routing algorithm outperforms the existing algorithm on both the temporal and spatial network performance. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Routing algorithms

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DPHL:A DIA Pan-human Protein Mass Spectrometry Library for Robust Biomarker Discovery

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Genomics, Proteomics & Bioinformatics 2020年第2期18卷 104-119页

作者： Tiansheng Zhu Yi Zhu Yue Xuan Huanhuan Gao Xue Cai Sander R.Piersma Thang V.Pham Tim Schelfhorst Richard R.G.D.Haas Irene V.Bijnsdorp Rui Sun Liang Yue Guan Ruan Qiushi Zhang Mo Hu Yue Zhou Winan J.Van Houdt Tessa Y.S.Le Large Jacqueline Cloos Anna Wojtuszkiewicz Danijela Koppers-Lalic Franziska Bottger Chantal Scheepbouwer Ruud H.Brakenhoff Geert J.L.H.van Leenders Jan N.M.Ijzermans John W.M.Martens Renske D.M.Steenbergen Nicole C.Grieken Sathiyamoorthy Selvarajan Sangeeta Mantoo Sze S.Lee Serene J.Y.Yeow Syed M.F.Alkaff Nan Xiang Yaoting Sun Xiao Yi Shaozheng Dai Wei Liu Tian Lu Zhicheng Wu Xiao Liang Man Wang Yingkuan Shao Xi Zheng Kailun Xu Qin Yang Yifan Meng Cong Lu Jiang Zhu Jin'e Zheng Bo Wang Sai Lou Yibei Dai Chao Xu Chenhuan Yu Huazhong Ying Tony K.Lim Jianmin Wu Xiaofei Gao Zhongzhi Luan Xiaodong Teng Peng Wu Shi'ang Huang Zhihua Tao Narayanan G.Iyer Shuigeng Zhou Wenguang Shao Henry Lam Ding Ma Jiafu Ji Oi L.Kon Shu Zheng Ruedi Aebersold Connie R.Jimenez Tiannan Guo Zhejiang Provincial Laboratory of Life Sciences and Biomedicine Westlake UniversityHangzhou 310024China Key Laboratory of Structural Biology of Zhejiang Province School of Life SciencesWestlake UniversityHangzhou 310024China Institute of Basic Medical Sciences Westlake Institute for Advanced StudyHangzhou 310024China School of Computer Science Shanghai Key Laboratory of Intelligent Information ProcessingFudan UniversityShanghai 200433China Thermo Fisher Scientific(BREMEN)GmbH Bremen 28195Germany OncoProteomics Laboratory Department of Medical OncologyVU University Medical CenterVU UniversityAmsterdam 1011The Netherlands Amsterdam UMC Vrije Universiteit AmsterdamUrologyCancer Center AmsterdamAmsterdam 1011The Netherlands Thermo Fisher Scientific Shanghai 201206China The Netherlands Cancer Institute Surgical OncologyAmsterdam 1011The Netherlands Amsterdam UMC Vrije Universiteit AmsterdamSurgeryCancer Center AmsterdamAmsterdam 1011The Netherlands Amsterdam UMC Vrije Universiteit AmsterdamPediatric Oncology/HematologyCancer Center AmsterdamAmsterdam 1011The Netherlands Amsterdam UMC Vrije Universiteit AmsterdamHematologyCancer Center AmsterdamAmsterdam 1011The Netherlands Amsterdam UMC Vrije Universiteit AmsterdamMedical OncologyCancer Center AmsterdamAmsterdam 1011The Netherlands Amsterdam UMC Vrije Universiteit AmsterdamNeurosurgeryCancer Center AmsterdamAmsterdam 1011The Netherlands Amsterdam UMC Vrije Universiteit AmsterdamPathologyCancer Center AmsterdamAmsterdam 1011The Netherlands Amsterdam UMC Vrije Universiteit AmsterdamOtolaryngology/Head and Neck SurgeryCancer Center AmsterdamAmsterdam 1011The Netherlands Erasmus MC University Medical Center PathologyRotterdam 1016LVThe Netherlands Erasmus MC University Medical Center SurgeryRotterdam 1016LVThe Netherlands Erasmus MC University Medical Center Medical OncologyRotterdam 1016LVThe Netherlands Department of Anatomical Pathology Singapore General HospitalSingapore 169608Singapore Division of Medical Scie

To address the increasing need for detecting and validating protein biomarkers in clinical specimens,mass spectrometry(MS)-based targeted proteomic techniques,including the selected reaction monitoring(SRM),parallel reaction monitoring(PRM),and massively parallel dataindependent acquisition(DIA),have been *** optimal performance,they require the fragment ion spectra of targeted peptides as prior *** this report,we describe a MS pipeline and spectral resource to support targeted proteomics studies for human tissue *** build the spectral resource,we integrated common open-source MS computational tools to assemble a freely accessible computational workflow based on *** then applied the workflow to generate DPHL,a comprehensive DIA pan-human library,from 1096 data-dependent acquisition(DDA)MS raw files for 16 types of cancer *** extensive spectral resource was then applied to a proteomic study of 17 prostate cancer(PCa)***,PRM validation was applied to a larger study of 57 PCa patients and the differential expression of three proteins in prostate tumor was *** a second application,the DPHL spectral resource was applied to a study consisting of plasma samples from 19 diffuse large B cell lymphoma(DLBCL)patients and 18 healthy control *** expressed proteins between DLBCL patients and healthy control subjects were detected by DIA-MS and confirmed by *** data demonstrate that the DPHL supports DIA and PRM MS pipelines for robust protein biomarker *** is freely accessible at https://***/page/***?id=IPX0001400000.

关键词： Data-independent acquisition Parallel reaction monitoring Spectral library Prostate cancer Diffuse large B cell lymphoma

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Deep web entity identification method with unique constraint

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International Journal of Performability engineering 2018年第10期14卷 2470-2482页

作者： Xian, Xuefeng Zhao, Pengpeng Liu, Zhaobin Gu, Caidong Sheng, Victor S. School of Computer Engineering Suzhou Vocational University Suzhou215104 China Institute of Intelligent Information Processing and Application Soochow University Suzhou215006 China Computer Science Department University of Central Arkansas Conway72035 United States

In practice, some attributes meet a unique constraint: each entity has a unique value for the attribute. A deep web entity identification method was presented to solve problems of data error correction, uniqueness constraint enforcement, and local data fusion in deep web data integration. The method transformed the entity identification phrase to a k-partite graph clustering problem, considering both similarity and association of attribute values. Moreover, it performed global record linkage and data fusion simultaneously and could identify incorrect values and differentiate them from correct ones at the beginning. Experimental results demonstrate the high precision and scalability of our method. © 2018 Totem Publisher, Inc. All rights reserved.

关键词： Data fusion

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Road surface friction prediction using long short-term memory neural network based on historical data

arXiv

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arXiv 2019年

作者： Pu, Ziyuan Wang, Shuo Liu, Chenglong Cui, Zhiyong Wang, Yinhai Smart Transportation Application and Research Laboratory Department of Civil and Environmental Engineering University of Washington 101 More Hall SeattleWA United States School of Computer Science and Engineering Nanjing University of Science and Technology 200 Xiaolingwei Street NanjingJiangsu China Key Laboratory of Road and Traffic Engineering of the Ministry of Education Tongji University 4800 Cao’an Road Shanghai China Smart Transportation Application and Research Laboratory Department of Civil and Environmental Engineering University of Washington 101 More Hall SeattleWA United States Department of Civil and Environmental Engineering University of Washington 121F More Hall SeattleWA United States

Road surface friction significantly impacts traffic safety and mobility. A precise road surface friction prediction model can help to alleviate the influence of inclement road condition on traffic safety, Level of Service, traffic mobility, fuel efficiency, and sustained economic productivity. Most related previous studies are laboratory-based methods which are difficult for practical implementation. Moreover, in other data-driven methods, the demonstrated time-series features of road surface condition have not been considered. This study employed a Long-Short Term Memory (LSTM) neural network to develop a data-driven road surface friction prediction model based on historical data. The proposed prediction model outperformed the other baseline models in terms of the lowest value of predictive performance measurements. The influence of the number of time-lags and the predicting time interval on predictive accuracy was analyzed. In addition, the influence of adding road surface water thickness, road surface temperature and air temperature on predictive accuracy also were investigated. The findings of this study can support the road maintenance strategy development and decision making, thus mitigating the impact of inclement road condition on traffic mobility and safety. Future work includes a modified LSTM-based prediction model development by accommodating flexible time interval between time-lags. Copyright © 2019, The Authors. All rights reserved.

关键词： Long short-term memory

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scTenifoldXct: A semi-supervised method for predicting cell-cell interactions and mapping cellular communication graphs

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Cell systems 2023年第4期14卷 302-311.e4页

作者： Yongjian Yang Guanxun Li Yan Zhong Qian Xu Yu-Te Lin Cristhian Roman-Vicharra Robert S Chapkin James J Cai Department of Electrical and Computer Engineering Texas A&M University College Station TX 77843 USA. Department of Statistics Texas A&M University College Station TX 77843 USA. Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE School of Statistics East China Normal University 3663 North Zhongshan Road Shanghai 200062 China. Department of Veterinary Integrative Biosciences Texas A&M University College Station TX 77843 USA. Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei Taiwan. Department of Nutrition and the Program in Integrative Nutrition & Complex Diseases Texas A&M University College Station TX 77843 USA. Electronic address: r-chapkin@tamu.edu. Department of Electrical and Computer Engineering Texas A&M University College Station TX 77843 USA Department of Veterinary Integrative Biosciences Texas A&M University College Station TX 77843 USA Interdisciplinary Program of Genetics Texas A&M University College Station TX 77843 USA. Electronic address: jcai@tamu.edu.

We present scTenifoldXct, a semi-supervised computational tool for detecting ligand-receptor (LR)-mediated cell-cell interactions and mapping cellular communication graphs. Our method is based on manifold alignment, using LR pairs as inter-data correspondences to embed ligand and receptor genes expressed in interacting cells into a unified latent space. Neural networks are employed to minimize the distance between corresponding genes while preserving the structure of gene regression networks. We apply scTenifoldXct to real datasets for testing and demonstrate that our method detects interactions with high consistency compared with other methods. More importantly, scTenifoldXct uncovers weak but biologically relevant interactions overlooked by other methods. We also demonstrate how scTenifoldXct can be used to compare different samples, such as healthy vs. diseased and wild type vs. knockout, to identify differential interactions, thereby revealing functional implications associated with changes in cellular communication status.

关键词： cell-cell interaction cellular communication gene regression network machine learning manifold alignment neural networks scRNA-seq single-cell RNA sequencing

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Recognition of facial expression based on CNN-CBP features 29

Recognition of facial expression based on CNN-CBP features

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第29届中国控制与决策会议

作者： Yize Liu Yixiang Chen School of Computer Science and Software Engineering MoE Engineering Center for Software/Hardware Co-Design Technology and ApplicationEast China Normal University

ISBN: (纸本)9781509046584

Automatical recognition of facial expression is an interesting and challenging problem,which has so many applications such as expression synthesis,human-robot interaction,metal state identification,intelligent tutoring systems,operator fatigue,music for mood,and clinical *** vital step of a successful approach is deriving features from raw facial *** existed methods of features extraction are the hand-crafted features based on geometric features or appearance features,and the auto-learned *** utilize the benefit of low computation of hand-crafted features and the high-representation of auto-learned features,we firstly proposed the combined features CNN-CBP with putting together Centralized Binary Patterns(CBP) features and Convolutional Neural Network(CNN) *** then,we classified the features using Support Vector Machine(SVM).With the help of the CNN-CBP features,we achieved average recognition accuracy of 97.6%on the Extended Cohn-Kanade datasets and 88.7%on the Japanese Femal Facial Expression datasets based on 10-cross validation.

关键词： Facial Expression Recognition Convolutional Neural Network Centralized Binary Patterns Combination of Features Support Vector Machine

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An Effective Retinal Blood Vessel Segmentation by Using Automatic Random Walks Based on Centerline Extraction

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BioMed Research International 2020年第0期2020卷 000-000页

作者： Gao, Jianqing Chen, Guannan Lin, Wenru Smart Home Information Collection and Processing on Internet of Things Laboratory of Digital Fujian Fujian Jiangxia University Fuzhou 350108 China School of Electronic Information Science Fujian Jiangxia University Fuzhou 350108 China Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education Fujian Provincial Key Laboratory for Photonics Technology Fujian Normal University Fuzhou 350007 China Fujian Provincial Engineering Technology Research Center of Photoelectric Sensing Application Fujian Normal University Fuzhou 350117 China College of Computer and Control Engineering Minjiang University Fuzhou 350121 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou 350121 China

The retinal blood vessel analysis has been widely used in the diagnoses of diseases by ophthalmologists. According to the complex morphological characteristics of the blood vessels in normal and abnormal images, an automatic method by using the random walk algorithms based on the centerlines is proposed to segment retinal blood vessels. Hessian-based multiscale vascular enhancement filtering is used to display the vessel structures in maximum intensity projection. Random walk algorithm provides a unique and quality solution, which is robust to weak object boundaries. Seed groups in the random walk segmentation are labeled according to the centerlines, which are extracted by using the divergence of the normalized gradient vector field and the morphological method. Experiments of the proposed method are implemented on the publicly available STARE (the Structured Analysis of the Retina) database. The results are compared to other existing retinal blood vessel segmentation methods with respect to the accuracy, sensitivity, and specificity, and the proposed method is proved to be more sensitive in detecting the retinal blood vessels in both normal and pathological areas. © 2020 Jianqing Gao et al.

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A multi-level deep ensemble model for skin lesion classification in dermoscopy images

arXiv

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arXiv 2018年

作者： Xie, Yutong Zhang, Jianpeng Xia, Yong National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University Xi'an710072 China

A multi-level deep ensemble (MLDE) model that can be trained in an 'end to end' manner is proposed for skin lesion classification in dermoscopy images. In this model, four pre-trained ResNet-50 networks are used to characterize the multiscale information of skin lesions and are combined by using an adaptive weighting scheme that can be learned during the error back propagation. The proposed MLDE model achieved an average AUC value of 86.5% on the ISIC-skin 2018 official validation dataset, which is substantially higher than the average AUC values achieved by each of four ResNet-50 networks. Copyright © 2018, The Authors. All rights reserved.

关键词： Image classification

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The design and implementation of intersection points of geometric figures in netpad 13

The design and implementation of intersection points of geom...

引用

13th International Conference on computer science and Education, ICCSE 2018

作者： Chen, Ruxian Rao, Yonesheng Guan, Hao Wang, Ying School of Computer Science and Education Software Guangzhou University Guangzhou China Guangdong Provincial Engineering and Technology Research Center for Mathematical Education Software Guangzhou China Institute of Computing Science and Technoligy Guangzhou University Guangzhou China Guizhou Provincal Academician Workstation of Educational Big Date Technology and Educational Mathematics Guiyang China University of the Chinese Academy of Sciences Beijing China Chengdu Institute of Computer Application Chinese Academy of Sciences Chengdu China Department of Network Technology South China Institute of Software Engineering Guangzhou China

ISBN: (纸本)9781538654958

It is a very important function of dynamic geometry software to construct intersection points of geometric figures. However, this function isn't perfect in most dynamic geometry software yet. In this paper, we design and implement intersection points of geometric figures in NetPad, an advanced online dynamic mathematics software designed for teaching and learning mathematics in elementary and secondary school. Under the design concepts of convenience, completion and stability, NetPad not only can conveniently construct intersection points of any geometric figures, but also maintain the continuity and stability of intersection points. Through some teaching cases, we demonstrate the simplicity and efficiency of constructing intersection points of geometric figures in NetPad. © 2018 IEEE.

关键词： Geometry

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