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Construction of the recognition model for trigger words of Chinese acupuncture and moxibustion events

Journal of information Hiding and Multimedia Signal processing 2017年第6期8卷 1283-1293页

作者： Sun, Shuihua Huang, Degen Zhang, Jing School of Computer Science and Technology Dalian University of Technology Dalian116024 China College of Information Science and Engineering Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou350118 China

The recognization of trigger words for Chinese acupuncture and moxibustion events is a key step in the extraction of Chinese acupuncture and moxibustion events. It plays an important role in knowledge mining in the field of Chinese acupuncture and moxibustion. This paper extracts the manually annotated trigger words from the training corpus, constructs the table of trigger words for Chinese acupuncture and moxibustion events, then expands this table with the Tongyici Cilin (Chinese thesaurus), and identifies the candidate trigger words for Chinese acupuncture and moxibustion events based on the extended trigger word table;and then according to characteristics of the expressions in the field of Chinese acupuncture and moxibustion, this paper prepares the filtering rules for candidate trigger words for Chinese acupuncture and moxibustion events. This paper uses the above techniques to construct the recognition model of trigger words for Chinese acupuncture and moxibustion events that integrates dictionary matching and rule-based filtering. The results of the experiment show that this model has a good trigger word recognition function, and F-measure of trigger words recognition for the cure and health events reaches 88.28% and 34.15% respectively. © 2017, Ubiquitous International. All rights reserved.

关键词： Vocabulary control

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Nuclear norm regularized coding with local position-patch and nonlocal similarity for face hallucination

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Digital Signal processing 2017年 64卷 107-120页

作者： Guangwei Gao Jian Yang Zhihui Lai Pu Huang Jielin Jiang Hao Gao Dong Yue Institute of Advanced Technology Nanjing University of Posts and Telecommunications Nanjing 210023 PR China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou 350121 PR China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing 210094 PR China College of Computer Science and Software Engineering Shenzhen University Shenzhen 518060 PR China Key Laboratory of Image and Video Understanding for Social Safety Nanjing University of Science and Technology Nanjing 210094 PR China School of Computer and Software Nanjing University of Information Science and Technology Nanjing 210044 PR China

Learning-based face hallucination methods have received much attention and progress in past few decades. Specially, position-patch based approaches have been proposed to replace the probabilistic graph-based or manifold learning-based ones. As opposed to the existing patch based methods, where the input image patch matrix is converted into vectors before combination coefficients calculation, in this paper, we propose to directly use the image matrix based regression model for combination coefficients computation to preserve the essential structural information of the input patch matrix. For each input low-resolution (LR) patch matrix, its combination coefficients over the training image patch matrices at the same position can be computed. Then the corresponding high-resolution (HR) patch matrix can be obtained with the LR training patches replaced by the corresponding HR ones. The nonlocal self-similarities are finally utilized to further improve the hallucination performance. Various experimental results on standard face databases indicate that our proposed method outperforms some state-of-the-art algorithms in terms of both visual quantity and objective metrics.

关键词： Face hallucination Position-patch Matrix based regression Nuclear norm Nonlocal similarity

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Combining Event-level and Cross-event Semantic information for Event-Oriented Relation Classification by SCNN

Combining Event-level and Cross-event Semantic Information f...

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第十五届全国计算语言学学术会议(CCL2016)暨第四届基于自然标注大数据的自然语言处理国际学术研讨会(NLP-NABD-2016)

作者： Siyuan Ding Yu Hong Shanshan Zhu Jianmin Yao Qiaoming Zhu Provincial Key Laboratory of Computer Information Processing Technology Soochow UniversitySuzhouChina

Previous researches on event relation classification primarily rely on lexical and syntactic *** this paper,we use a Shallow Convolutional Neural Network(SCNN)to extract event-level and cross-event semantic features for event relation *** the one hand,the shallow structure alleviates the over-fitting problem caused by the lack of diverse relation *** the other hand,the utilization and combination of event-level and cross-event semantic information help improve relation *** experimental results show that our approach outperforms the state of the art.

关键词： Event Relation Classification Semantic information Frame Embedding SCNN

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A novel image segmentation method based on an improved bacterial foraging optimization algorithm

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Journal of information Hiding and Multimedia Signal processing 2017年第2期8卷 348-357页

作者： Zeng, Zhi Gao Guan, Liang Hua Zhu, Yan Hui Liu, Qiang He, Jin Rong College of Computer and Communication Hunan University of Technology ZhuzhouHunan412000 China Intelligent Information Perception and Processing Technology Hunan Province Key Laboratory China College of Information Engineering Northwest A and F University YanglingShaanxi Province712100 China

When some bionic optimization algorithms are used for image segmentation, we find that the search speeds of these algorithms are slow and the local searching abilities of these algorithms need be improved. In order to solve these problems, this paper proposed a new image segmentation method based on the improved bacterial foraging optimization algorithm. Firstly, a dynamic step size is used to instead of the fixed step size of the chemotaxis operator, and a dynamic probability is used to instead of the fixed probability of elimination-dispersal operator. Then, the gray histograms of the images are extracted for the image segmentation. Ultimately, the images are segmented using the improved bacterial foraging optimization algorithm. The image segmentation results show that the accuracy and the speed of the image segmentation based on the improved bacterial foraging optimization algorithm are superior to the ones that based on others traditional bionic optimization algorithms. © 2017 ISSN.

关键词： Image segmentation

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Research on Ship Automatic Modeling and Identification System

Research on Ship Automatic Modeling and Identification Syste...

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2017 2nd International Conference on Mechatronics and information technology(ICMIT 2017)

作者： Yao-jie Chen Xin Yuan Shu-lin Du Can Song School of Computer Science and Technology Wuhan University of Science and Technology School of Navigation Wuhan University of Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System

When the ship sails in different water situation, it is necessary to forecast the movement state of the ship according to the real-time ship motion state parameters. The traditional method uses the classical MMG(mathematical model group) three-degree-of-freedom motion mathematical model to predict the modeling. For the existence of less detection parameters, the error is relatively large, the accuracy is not enough and so on. In this paper, a six-degree-of-freedom ship motion model based on sway, surge, heave, roll, pitch and yawing is proposed, combined with the least square method to achieve the automatic identification modeling method. Using this method, according to the GPS inertial navigation and positioning module to collect the data to simulate, and contrast with the traditional modeling method. The experimental results show that the improved automatic identification modeling method has better effect than the traditional modeling method, which greatly improves the accuracy of ship motion prediction.

关键词： automatic identification six degrees of freedom least square method

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A throughput aware with collision-free MAC for wireless LANs

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Science China(information Sciences) 2016年第2期59卷 240-242页

作者： Tingrui PEI Yafeng DENG Zhetao LI Gengming ZHU Gaofeng PAN Youngjune CHOI Hiroo SEKIYA College of Information Engineering Xiangtan University Key Laboratory of Intelligent Computing & Information Processing of Ministry of Education Xiangtan University School of Computer Science & Engineering Hunan University of Science and Technology School of Electronic and Information Engineering Southwest University Department of Information and Computer Engineering Ajou University Graduate School of Advanced Integration Science Chiba University

Dear editor,The Carrier Sense Multiple Access with Collision Avoidance(CSMA/CA)hybrid with the resource reservation approach from Time Division Multiple Address(TDMA)has been emerged as a promising method to solve collision problems in wireless LANs[1–6].In the hybrid method,a TDMA circle contains multiple slots for nodes to contend for the ***,it allows a

关键词： MAC CSCF free TDMA A throughput aware with collision-free MAC for wireless LANs

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Survey of key technologies on millimeter-wave CMOS integrated circuits

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IOP Conference Series: Materials Science and Engineering 2018年第1期351卷

作者： Fei Yu Lei Gao Lixiang Li Shuo Cai Wei Wang Chunhua Wang Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation & School of Computer and Communication Engineering Changsha University of Science and Technology Changsha 410114 China College of Information Science and Engineering Hunan University Changsha 410082 China

In order to provide guidance for the development of high performance millimeter-wave complementary metal oxide semiconductor (MMW-CMOS) integrated circuits (IC), this paper provides a survey of key technologies on MMW-CMOS IC. Technical background of MMW wireless communications is described. Then the recent development of the critical technologies of the MMW-CMOS IC are introduced in detail and compared. A summarization is given, and the development prospects on MMW-CMOS IC are also discussed.

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Bayesian multi-hypothesis Wyner-Ziv video coding

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Journal of information Hiding and Multimedia Signal processing 2017年第2期8卷 478-485页

作者： Meng, Li Li Zong, Jing Xiu Sun, Gui Na Cheng, Jie Zhang, Jia Zhao, Meng Chen Department of Information Science and Engineering Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Shandong Normal University No.88 Wenhua East Road Jinan250014 China

The performance of distributed video coding (DVC) relies heavily on the quality of the side information (SI), and better performance can be expected if multiple SIs are employed. In this paper, we consider the scenario with two SIs, which are obtained by forward and backward predictions respectively. In this case, a couple of ad hoc approaches have been proposed to approximate the conditional probability density function (pdf) of a Wyner-Ziv-coded frame given the two SIs. However, the optimal conditional pdf and the relative performances of these ad hoc approaches with respect to the optimal solution have not been studied. In this paper, using Bayes’ formula, we derive the closed-form expression of the conditional pdf. Experimental results show that the Bayesian solution has similar performance to previous methods. Moreover, simulation results reveal that when better SIs are available, the Bayesian solution could outperform other methods. This analysis sheds some lights on developing future DVC schemes. © 2017 ISSN.

关键词： Probability density function

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Active transfer learning of matching query results across multiple sources

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Frontiers of computer Science 2015年第4期9卷 595-607页

作者： Jie XIN Zhiming CUI Pengpeng ZHAO Tianxu HE The Institute of Intelligent Information Processing and Application Soochow University Suzhou 215006 China Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou 215006 China

Entity resolution （ER） is the problem of identi- fying and grouping different manifestations of the same real world object. Algorithmic approaches have been developed where most tasks offer superior performance under super- vised learning. However, the prohibitive cost of labeling training data is still a huge obstacle for detecting duplicate query records from online sources. Furthermore, the unique combinations of noisy data with missing elements make ER tasks more challenging. To address this, transfer learning has been adopted to adaptively share learned common structures of similarity scoring problems between multiple sources. Al- though such techniques reduce the labeling cost so that it is linear with respect to the number of sources, its random sam- piing strategy is not successful enough to handle the ordinary sample imbalance problem. In this paper, we present a novel multi-source active transfer learning framework to jointly select fewer data instances from all sources to train classi- fiers with constant precision/recall. The intuition behind our approach is to actively label the most informative samples while adaptively transferring collective knowledge between sources. In this way, the classifiers that are learned can be both label-economical and flexible even for imbalanced or quality diverse sources. We compare our method with the state-of-the-art approaches on real-word datasets. Our exper- imental results demonstrate that our active transfer learning algorithm can achieve impressive performance with far fewerlabeled samples for record matching with numerous and var- ied sources.

关键词： entity resolution active learning transfer learn-ing convex optimization

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Estimates, trends, and drivers of the global burden of type 2 diabetes attributable to PM_2·5 air pollution, 1990–2019: an analysis of data from the Global Burden of Disease Study 2019

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The Lancet Planetary Health 2022年第7期6卷 e586-e600页

作者： Burkart, Katrin Causey, Kate Cohen, Aaron J Wozniak, Sarah S Salvi, Devashri Digvijay Abbafati, Cristiana Adekanmbi, Victor Adsuar, Jose C Ahmadi, Keivan Alahdab, Fares Al-Aly, Ziyad Alipour, Vahid Alvis-Guzman, Nelson Amegah, Adeladza Kofi Andrei, Catalina Liliana Andrei, Tudorel Ansari, Fereshteh Arabloo, Jalal Aremu, Olatunde Aripov, Timur Babaee, Ebrahim Banach, MacIej Barnett, Anthony Bärnighausen, Till Winfried Bedi, Neeraj Behzadifar, Masoud Béjot, Yannick Bennett, Derrick A Bensenor, Isabela M Bernstein, Robert S Bhattacharyya, Krittika Bijani, Ali Biondi, Antonio Bohlouli, Somayeh Breitner, Susanne Brenner, Hermann Butt, Zahid A Cámera, Luis Alberto Cantu-Brito, Carlos Carvalho, Felix Cerin, Ester Chattu, Vijay Kumar Chauhan, Bal Govind Choi, Jee-Young Jasmine Chu, Dinh-Toi Dai, Xiaochen Dandona, Lalit Dandona, Rakhi Daryani, Ahmad Davletov, Kairat De Courten, Barbora Demeke, Feleke Mekonnen Denova-Gutiérrez, Edgar Dharmaratne, Samath Dhamminda Dhimal, Meghnath Diaz, Daniel Djalalinia, Shirin Duncan, Bruce B El Sayed Zaki, Maysaa Eskandarieh, Sharareh Fareed, Mohammad Farzadfar, Farshad Fattahi, Nazir Fazlzadeh, Mehdi Fernandes, Eduarda Filip, Irina Fischer, Florian Foigt, Nataliya A Freitas, Marisa Ghashghaee, Ahmad Gill, Paramjit Singh Ginawi, Ibrahim Abdelmageed Gopalani, Sameer Vali Guo, Yuming Gupta, Rajat Das Habtewold, Tesfa Dejenie Hamadeh, Randah R Hamidi, Samer Hankey, Graeme J Hasanpoor, Edris Hassen, Hamid Yimam Hay, Simon I Heibati, Behzad Hole, Michael K Hossain, Naznin Househ, Mowafa Irvani, Seyed Sina Naghibi Jaafari, Jalil Jakovljevic, Mihajlo Jha, Ravi Prakash Jonas, Jost B Jozwiak, Jacek Jerzy Kasaeian, Amir Kaydi, Neda Khader, Yousef Saleh Khafaie, Morteza Abdullatif Khan, Ejaz Ahmad Khan, Junaid Khan, Md Nuruzzaman Khatab, Khaled Khater, Amir M Kim, Yun Jin Kimokoti, Ruth W Kisa, Adnan Kivimäki, Mika Knibbs, Luke D Kosen, Soewarta Koul, Parvaiz A Koyanagi, Ai Defo, Barthelemy Kuate Kugbey, Nuworza Lauriola, Paolo Lee, Paul H Leili, Mostafa Lewycka, Sonia Li, Shanshan Lim, Lee-Ling Li Institute for Health Metrics and Evaluation University of Washington Seattle WA United States Department of Health Metrics Sciences School of Medicine University of Washington Seattle WA United States Division of Plastic and Reconstructive Surgery University of Washington Seattle WA United States Research Insights and Innovation Seattle WA United States Health Effects Institute Boston MA United States School of Public Health Boston University Boston MA United States Department of Juridical and Economic Studies La Sapienza University Rome Italy Department of Population Medicine Cardiff University Cardiff United Kingdom Sport Science Department University of Extremadura Badajoz Spain School of Public Health Imperial College London London United Kingdom Department of Primary Care and Public Health Imperial College London London United Kingdom Mayo Evidence-based Practice Center Mayo Clinic Foundation for Medical Education and Research Rochester MN United States John T. Milliken Department of Internal Medicine Washington University in St. Louis St. Louis MO United States Clinical Epidemiology Center US Department of Veterans Affairs (VA) St Louis MO United States Health Management and Economics Research Center Iran University of Medical Sciences Tehran Iran Department of Health Economics Iran University of Medical Sciences Tehran Iran Preventive Medicine and Public Health Research Center Iran University of Medical Sciences Tehran Iran Pars Advanced and Minimally Invasive Medical Manners Research Center Iran University of Medical Sciences Tehran Iran Department of Community and Family Medicine Iran University of Medical Sciences Tehran Iran Research Group in Hospital Management and Health Policies Universidad de la Costa (University of the Coast) Barranquilla Colombia Research Group in Health Economics University of Cartagena Cartagena Colombia Department of Biomedical Science University of Cape Coast Cape Coast Ghana Cardiology Department Ca

Background: Experimental and epidemiological studies indicate an association between exposure to particulate matter (PM) air pollution and increased risk of type 2 diabetes. In view of the high and increasing prevalence of diabetes, we aimed to quantify the burden of type 2 diabetes attributable to PM_2·5 originating from ambient and household air pollution. Methods: We systematically compiled all relevant cohort and case-control studies assessing the effect of exposure to household and ambient fine particulate matter (PM_2·5) air pollution on type 2 diabetes incidence and mortality. We derived an exposure–response curve from the extracted relative risk estimates using the MR-BRT (meta-regression—Bayesian, regularised, trimmed) tool. The estimated curve was linked to ambient and household PM_2·5 exposures from the Global Burden of Diseases, Injuries, and Risk Factors Study 2019, and estimates of the attributable burden (population attributable fractions and rates per 100 000 population of deaths and disability-adjusted life-years) for 204 countries from 1990 to 2019 were calculated. We also assessed the role of changes in exposure, population size, age, and type 2 diabetes incidence in the observed trend in PM_2·5-attributable type 2 diabetes burden. All estimates are presented with 95% uncertainty intervals. Findings: In 2019, approximately a fifth of the global burden of type 2 diabetes was attributable to PM_2·5 exposure, with an estimated 3·78 (95% uncertainty interval 2·68–4·83) deaths per 100 000 population and 167 (117–223) disability-adjusted life-years (DALYs) per 100 000 population. Approximately 13·4% (9·49–17·5) of deaths and 13·6% (9·73–17·9) of DALYs due to type 2 diabetes were contributed by ambient PM2·5, and 6·50% (4·22–9·53) of deaths and 5·92% (3·81–8·64) of DALYs by household air pollution. High burdens, in terms of numbers as well as rates, were estimated in Asia, sub-Saharan Africa, and South Am

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