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Gestational Diabetes Mellitus Prediction Based on Two Classification Algorithms

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Gestational Diabetes Mellitus Prediction Based on Two Classi...

International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)

作者： Weiyang Zhong Shanxi Jiang Wei Wu Danhong Peng Tian Xu Jun Wang Gongdao Wang Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province Nanjing University of Posts and Telecommunications Nanjing PR China China Tower Corporation Limited Nantong Branch PR China Key Laboratory of Environment Medicine and Engineering Ministry of Education School of Public Health Southeast University Nanjing PR China Zhongda Hospital Southeast University NanjingSchool of Science China Pharmaceutical University Nanjing PR China

Gestational diabetes mellitus (GDM) is a disease with normal glucose tolerance before pregnancy and only diabetes during pregnancy. The discovery of GDM has a long history. The specific causes and mechanisms of its occurrence are still unclear. There is a lack of research on intelligent diagnosis of GDM. GDM has many adverse effects on pregnant women and fetuses, which is of great significance for the early diagnosis of GDM. Based on the measured data of the hospital, this paper realizes the intelligent diagnosis of GDM by using a improved KNN algorithm and a improved BP neural network.

关键词： Classification algorithms Training Neural networks Diabetes Pregnancy Genetic algorithms Sociology

Effect Analysis of Factors Based on Neural Network in Non-Contact Electrostatic Discharge 5

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Effect Analysis of Factors Based on Neural Network in Non-Co...

The 5th International Symposium on Electromagnetic Compatibility

作者： Huang Jun Ruan Fangming Su Ming Wang Heng YangXiangdong David Pommerenke Department of Big Data and Electronic Sciences Guizhou Normal University Beijing Oriental Institute of Metrology and Test EMC Lab of Missouri University of Science & Technology

ISBN: (纸本)9781509051861;9781509051854

Discharge parameters in non-contact electrostatic dtscharge(ESD) are affected fay various factors,including electrode moving speed to the target,gas pressure,temperature,*** of non-contact electrostatic discharge was analyzed based on a neural network model and compared to current waveform in non-contact electrostatic discharge measured with new measurement system of electrostatic *** network method was used to adjust the weight of discharge parameters in non-contact electrostatic discharge based on discharge current waveform affected by electrode moving speed,gas pressure,temperature and humidity,so as to compare with the experiment results waveforms met to the requirement of international standard IEC61000-4-2,and to analyze the main parameters that affect discharge currents in non-contact electrostatic discharge events.

关键词： component Electrostatic Discharge Gas Pressure ElectrodeMovingSpeed Neural Network Discharge Parameters

Growth Onset Rather Than Photosynthesis Strongly Regulates Autumn Senescence Termination Besides Climate Change

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Global Ecology and Biogeography 2025年第5期34卷

作者： Ji, Shuping Ren, Shilong Vitasse, Yann Zohner, Constantin M. Fu, Yongshuo H. Chen, Xiaoqiu Zhang, Xiaoyang Grossiord, Charlotte Liu, Huiying Peichl, Matthias Peng, Dailiang An, Shuai Li, Yating Li, Maihe Fang, Lei Chen, Jinyue Wang, Xinfeng Zhang, Qingzhu Wang, Guoqiang Wang, Qiao Big Data Research Center for Ecology and Environment Environment Research Institute Shandong University Qingdao China WSL Swiss Federal Institute for Forest Snow and Landscape Research Birmensdorf Switzerland Institute of Integrative Biology ETH Zurich (Swiss Federal Institute of Technology) Zurich Switzerland College of Water Sciences Beijing Normal University Beijing China College of Urban and Environmental Sciences Laboratory for Earth Surface Processes of the Ministry of Education Peking University Beijing China Geospatial Science Center of Excellence Department of Geography and Geospatial Sciences South Dakota State University Brookings SD United States Plant Ecology Research Laboratory (PERL) École Polytechnique Fédérale de Lausanne (EPFL) Lausanne Switzerland Tiantong National Station for Forest Ecosystem Research Center for Global Change and Ecological Forecasting The Shanghai Key Lab for Urban Ecological Processes and Eco-Restoration School of Ecological and Environmental Sciences East China Normal University Shanghai China Department of Forest Ecology and Management Swedish University of Agricultural Sciences (SLU) Umeå Sweden Key Laboratory of Digital Earth Science Aerospace Information Research Institute Chinese Academy of Sciences Beijing China Department of Environmental Sciences University of Basel Basel Switzerland Key Laboratory of Geographical Processes and Ecological Security in Changbai Mountains Ministry of Education School of Geographical Sciences Northeast Normal University Jilin China College of Life Science Hebei University Hebei Baoding China Center for Geodata and Analysis Faculty of Geographical Science Beijing Normal University Beijing China

Aim: Plant senescence largely influences the global carbon cycle by regulating the growing season length. However, the driving mechanisms of plant senescence remain unclear, particularly the role of developmental factors. This study aims to investigate how environmental and developmental factors drive autumn senescence and evaluate whether woody and herbaceous plants exhibit divergent responses to these drivers. Location: Eurasia. Time Period: 1982–2014. Major Taxa Studied: Woody and herbaceous species. Methods: Using 120,833 long-term ground phenological observations, we employed partial correlation analysis to investigate the influence of environmental and developmental factors on senescence termination. Experimental records from literature and pasture survey observations from China were separately utilised to further validate the influence of developmental factors on senescence termination. Structural equation modelling was applied to analyse the pathways of growth onset affecting senescence termination. Additionally, multiple linear regression was used to examine the tendency of the sensitivity of senescence termination to plant development rate. Results: We find that earlier growth onset primarily leads to earlier senescence termination directly in herbaceous plants, but indirectly in woody plants by accelerating early-season development. The sensitivity of senescence termination to plant development rate shows a declining trend, particularly in early-season negative effects on woody plants and late-season positive effects on herbaceous plants, suggesting diminished impacts of future warming on senescence timing. The impact of growing season photosynthetic activity on senescence termination is not pronounced for both woody and herbaceous plants. Main Conclusions: The results demonstrate that growth onset may affect woody and herbaceous senescence termination through different pathways, whereas the carry-over effects of growing season photosynthetic activity are

关键词： global warming growth onset herbaceous plants photosynthesis plant senescence woody plants

Automatic Chinese Multiple-Choice Question Generation for Human Resource Performance Appraisal

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Procedia Computer science 2018年 139卷 165-172页

作者： Pei Quan Yong Shi Lingfeng Niu Ying Liu Tianlin Zhang School of Computer and Control Engineering University of Academy of Sciences Beijing 100190 China School of Economics and Management University of Chinese Academy of Sciences Beijing 100190 China Key Lab of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing 100190 China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing 100190 China College of Information Science and Technology University of Nebraska at Omaha NE 68182 USA

Performance appraisal has always been an important research topic in human resource management. A reasonable performance appraisal plan lays a solid foundation for the development of an enterprise. Especially as globalization and technology advance, in order to meet the fast-changing strategic goals and increasing cross-functional tasks, enterprises face new challenges in performance appraisal. How to improve employees’ ability to accept new knowledge efficiently and constantly has been an urgent problem for enterprises. In this paper, we propose an automatic method which generation multiple-choice questions by utilizing the relations between different terminology. Graphical model is used to extract core concept from different corpus while word embedding technology is used to indicate the relevant relations. Experimental results demonstrate that the proposed question generation method outperforms the traditional manual method in both efficiency and confusion.

关键词： Human resource management multiple-choice question generation natural language processing word embedding

Corrigendum to “Promotion and resignation in employee networks” [Physica A 444 (2016) 442–447]

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Physica A: Statistical Mechanics and its Applications 2020年 555卷

作者： Jia Yuan Qian-Ming Zhang Jian Gao Linyan Zhang Xue-Song Wan Xiao-Jun Yu Tao Zhou Chengdu Institute of Public Administration Chengdu 610110 People’s Republic of China School of Management and Economics University of Electronic Science and Technology of China Chengdu 611731 People’s Republic of China CompleX Lab Web Sciences Center School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu 611731 People’s Republic of China Center for Polymer Studies Department of Physics Boston University Boston 02215 United States Hire Big Data (Chengdu) LTD. Chengdu 611731 People’s Republic of China Beijing Strong Union Technology Co. Ltd. Beijing 100086 People’s Republic of China Big Data Research Center University of Electronic Science and Technology of China Chengdu 611731 People’s Republic of China

Structured query construction via knowledge graph embedding

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arXiv 2019年

作者： Wang, Ruijie Wang, Meng Liu, Jun Cochez, Michael Decker, Stefan National Engineering Lab for Big Data Analytics Xi'an Jiaotong University Xi'an China School of Electronic and Information Engineering Xi'an Jiaotong University Xi'an China School of Computer Science and Engineering Southeast University Nanjing China Guang Dong Xi'an Jiaotong University Academy Shunde China VU Amsterdam Amsterdam Netherlands Fraunhofer FIT Sankt Augustin Germany Informatik 5 RWTH Aachen University Aachen Germany Faculty of Information Technology University of Jyvskyl Jyvskyl Finland

In order to facilitate the accesses of general users to knowledge graphs, an increasing effort is being exerted to construct graph-structured queries of given natural language questions. At the core of the construction is to deduce the structure of the target query and determine the vertices/edges which constitute the query. Existing query construction methods rely on question understanding and conventional graph-based algorithms which lead to inefficient and degraded performances facing complex natural language questions over knowledge graphs with large scales. In this paper, we focus on this problem and propose a novel framework standing on recent knowledge graph embedding techniques. Our framework first encodes the underlying knowledge graph into a low-dimensional embedding space by leveraging generalized local knowledge graphs. Given a natural language question, the learned embedding representations of the knowledge graph are utilized to compute the query structure and assemble vertices/edges into the target query. Extensive experiments were conducted on the benchmark dataset, and the results demonstrate that our framework outperforms state-of-the-art baseline models regarding effectiveness and efficiency. Copyright © 2019, The Authors. All rights reserved.

关键词： Knowledge graph

Enhancing person-Job fit for talent recruitment: An ability-aware neural network approach

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arXiv 2018年

作者： Qin, Chuan Zhu, Hengshu Xu, Tong Zhu, Chen Jiang, Liang Chen, Enhong Xiong, Hui Anhui Province Key Lab of Big Data Analysis and Application University of Science and Technology of China Baidu Talent Intelligence Center Baidu Inc Business Intelligence Lab Baidu Research National Engineering Laboratory of Deep Learning Technology an Application China

The wide spread use of online recruitment services has led to information explosion in the job market. As a result, the recruiters have to seek the intelligent ways for Person-Job Fit, which is the bridge for adapting the right job seekers to the right positions. Existing studies on Person-Job Fit have a focus on measuring the matching degree between the talent qualification and the job requirements mainly based on the manual inspection of human resource experts despite of the subjective, incomplete, and inefficient nature of the human judgement. To this end, in this paper, we propose a novel end-to-end Ability-aware Person-Job Fit Neural Network (APJFNN) model, which has a goal of reducing the dependence on manual labour and can provide better interpretation about the fitting results. The key idea is to exploit the rich information available at abundant historical job application data. Specifically, we propose a word-level semantic representation for both job requirements and job seekers’ experiences based on Recurrent Neural Network (RNN). Along this line, four hierarchical ability-aware attention strategies are designed to measure the different importance of job requirements for semantic representation, as well as measuring the different contribution of each job experience to a specific ability requirement. Finally, extensive experiments on a large-scale real-world data set clearly validate the effectiveness and interpretability of the APJFNN framework compared with several baselines. Copyright © 2018, The Authors. All rights reserved.

关键词： Recurrent neural networks

VulDeePecker: A deep learning-based system for vulnerability detection

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arXiv 2018年

作者： Li, Zhen Zou, Deqing Xu, Shouhuai Ou, Xinyu Jin, Hai Wang, Sujuan Deng, Zhijun Zhong, Yuyi Services Computing Technology and System Lab Big Data Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology School of Cyber Security and Computer Hebei University Shenzhen Huazhong University of Science and Technology Research Institute Department of Computer Science University of Texas at San Antonio

The automatic detection of software vulnerabilities is an important research problem. However, existing solutions to this problem rely on human experts to define features and often miss many vulnerabilities (i.e., incurring high false negative rate). In this paper, we initiate the study of using deep learning-based vulnerability detection to relieve human experts from the tedious and subjective task of manually defining features. Since deep learning is motivated to deal with problems that are very different from the problem of vulnerability detection, we need some guiding principles for applying deep learning to vulnerability detection. In particular, we need to find representations of software programs that are suitable for deep learning. For this purpose, we propose using code gadgets to represent programs and then transform them into vectors, where a code gadget is a number of (not necessarily consecutive) lines of code that are semantically related to each other. This leads to the design and implementation of a deep learning-based vulnerability detection system, called Vulnerability Deep Pecker (VulDeePecker). In order to evaluate VulDeePecker, we present the first vulnerability dataset for deep learning approaches. Experimental results show that VulDeePecker can achieve much fewer false negatives (with reasonable false positives) than other approaches. We further apply VulDeePecker to 3 software products (namely Xen, Seamonkey, and Libav) and detect 4 vulnerabilities, which are not reported in the National Vulnerability database but were "silently" patched by the vendors when releasing later versions of these products;in contrast, these vulnerabilities are almost entirely missed by the other vulnerability detection systems we experimented with. Copyright © 2018, The Authors. All rights reserved.

关键词： Computer software

Non-reciprocal Cavity Polariton with Atoms Strongly Coupled to Optical Cavity

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arXiv 2019年

作者： Yang, Pengfei Li, Ming Han, Xing He, Hai Li, Gang Zou, Chang-Ling Zhang, Pengfei Qian, Yuhua Zhang, Tiancai State Key Laboratory of Quantum Optics and Quantum Optics Devices Institute of Opto-Electronics Shanxi University Taiyuan030006 China Cas Key Laboratory of Quantum Information University of Science and Technology of China Anhui Hefei230026 China Collaborative Innovation Center of Extreme Optics Shanxi University Taiyuan030006 China Institute of Big Data Science and Industry Shanxi University Taiyuan030006 China Key Lab. of Computational Intelligence and Chinese Information Processing of Ministry of Education Shanxi University Taiyuan030006 China

Breaking the time-reversal symmetry of light is of great importance for fundamental physics and has attracted increasing interest in the study of non-reciprocal photonic devices. Here, we experimentally demonstrate a chiral cavity QED system with multiple atoms strongly coupled to a Fabry-Pérot cavity. By polarizing the internal quantum state of the atoms, the time-reversal symmetry of the atom-cavity interaction is broken. The strongly coupled atom-cavity system can be described by non-reciprocal quasiparticles, i.e., the cavity polariton. When it works in the linear regime, the inherent nonreciprocity makes the system work as a single-photon-level optical isolator. Benefiting from the collective enhancement of multiple atoms, an isolation ratio exceeding 30 dB on the single-quanta level (∼ 0.1 photon on average) is achieved. The validity of the non-reciprocal device under zero magnetic field and the reconfigurability of the isolation direction are also experimentally demonstrated. Moreover, when the cavity polariton works in the nonlinear regime, the quantum interference between polaritons with weak anharmonicity induces non-reciprocal nonclassical statistics of cavity transmission from coherent probe light. Copyright © 2019, The Authors. All rights reserved.

关键词： Atoms