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Modeling Analysis and Comparision of Neural Network Simulation Based on ECM and LSTM

Journal of Physics: Conference Series 2021年第1期2068卷

作者： Lingyun Duan Ziyuan Liu Wen Yu Wei Chen Dongyan Jin Denghua Li Suhua Sun Ruixi Dai Agricultural Information Institute Chinese Academy of Agricultural Sciences Beijing 100081 P.R. China Key Laboratory of Agricultural Information Service Technology Ministry of Agriculture and Rural Affairs Beijing 100081 P.R. China Key Laboratory of Intelligent Agricultural Early Warning Technology and System Chinese Academy of Agricultural Sciences Beijing 100081 P.R. China Beijing Engineering Research Center for Agricultural Monitoring and Early Warning Beijing 100081 P.R. China Graduate School of Chinese Academy of Agricultural Sciences Beijing 100081 P.R. China

Comparing the prediction effects of traditional econometric algorithm model and deep learning algorithm model, taking regional GDP as an example, two prediction models of ARMA-ECM and LSTM-SVR are established for prediction, and the prediction results of different models are compared and analyzed. The results show that there are some deviations in the prediction results of the two models, but the prediction trends are the same. The prediction accuracy of LSTM-SVR model will decrease significantly with the reduction of time series data samples, while ARMA-ECM model is not so sensitive.

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Value based Curriculum Design for Engineering Studies 5

Value based Curriculum Design for Engineering Studies

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5th IEEE International Conference on MOOCs, Innovation and technology in Education (MITE)

作者： Subramanian, Shriram Dorairaju, Bharathi Pallavarapu, Mayur Department of Computer Science Tianjin University of Technology Tianjin China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin China Key Laboratory of Computer Vision and System Ministry of Education Tianjin China

ISBN: (纸本)9781538631898

Education apart from the basic needs of food, clothing and shelter, is another key factor to empower an individual. It acts as an enabler to elevate a person socially and from within. Education system has been evolving since human existence and various approaches are being used to teach. Focusing on professional courses, the quality of education is questioned and stands as a concern along with the unemployment issues. In India, there is a huge potential for technology growth. However, as per the National employability report, only 3.84% of engineers qualify for a startup technology role. Further, 2016 employability rate has been 17.91%, 3.67% and 40.57% for IT services, IT Products and Business Process Outsourcing respectively. Apart from this, there are many forums in which the quality of education is being questioned, since education is linked with the job or business opportunity. One of the essentials in the professional courses is the curriculum design. It is very important to start with the curriculum that can enhance the knowledge of students and also make a prospective career. With these in mind, the improvement options have to be deeply thought and charted out. In this regard, a survey has been taken up with engineering professionals to get their feedback on how they view the education system. This paper focuses on the feedback from working engineering professionals, compares the existing and other curriculum options and provides a proposal for a value based curriculum design.

关键词： Education system quality career engineering studies employability value based education curriculum

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An Event Timeline Extraction Method Based on News Corpus 2

An Event Timeline Extraction Method Based on News Corpus

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2017 IEEE 2nd International Conference on Big Data Analysis（ICBDA 2017）

作者： Yaguang Wu Haichun Sun Chungang Yan Department of Computer Science and Technology Tongji University The Key Laboratory of Embedded System and Service Computing Tongji University College of Police Information Engineering People's Public Security University of China

ISBN: (纸本)9781509036202

Event extraction is an important research point in information extraction area, and news event extraction has a greater practical significance. The existing methods of extracting news event, which starts from the time element, is to identify the date sentences hold by Natural Language Processing and extract the event on the date by Text Clustering. However, they only process the news that holds a time-tag and gives up the news that has no accurate date, which easily leads an extraction deviation of significant events and reduces the accuracy of ordering the significant events. In this paper, improvement for this defect is to calculate the similarities between sentences to put part of the non-time-tag sentences into the right date container, thereby the accuracy of ordering the significant event is *** experiments and compared with the existing methods,the accuracy of ordering the significant events in this paper is improved by 14.6%.

关键词： event extraction text information mining sentence similarity timeline

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Dynamic Simulation Test for Traveling Wave Fault Positioning system in Distribution Network 2

Dynamic Simulation Test for Traveling Wave Fault Positioning...

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2nd IEEE International Electrical and Energy Conference, CIEEC 2018

作者： Zhuo, Chao Ni, Yanru Zeng, Xiangjun Leng, Yang Xiang, Yunkun Guangxi Key Laboratory of Power System Optimization and Energy Technology Guangxi University Nanning Guangxi Zhuang Autonomous Region China Collaborative Innovation Center of Clean Energy and Electric Power Technology Changsha University of Science and Technology Changsha China State Grid Hunan Energy Conservation Service Company State Grid Hunan Electric Power Company Changsha China

ISBN: (纸本)9781538653913

Fault positioning system based on traveling wave has been widely used in engineering application, but the test technology still needs further study. The research status of test methods was introduced in this paper, and the reason of existing test technology is not suitable for fault location system was researched. Then the idea of dynamic simulation test system was put forward, including simulation methods of distribution lines, fault signal and traveling wave signal acquisition. Thus, a 10kV distribution network dynamic simulation test model was built, and dynamic simulation test scheme for fault positioning system was designed, introducing the fault setting method and the system function test project in detail. The experimental results showed that the dynamic simulation test platform is close to the actual operation environment of the distribution network, which can effectively estimate technical indicators of the voltage traveling wave positioning system into operation. © 2018 IEEE.

关键词： Signal processing

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Raptor: A Five-Safes Approach to a Secure, Cloud Native and Serverless Genomics Data Repository

SSRN

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SSRN 2022年

作者： Shih, Chih Chuan Chen, Jieqi Lee, Ai Shan Bertin, Nicolas Hebrard, Maxime Khor, Chiea Chuen Li, Zheng Tan, Joanna Hui Juan Meah, Wee Yang Peh, Su Qin Mok, Shi Qi Sim, Kar Seng Liu, Jianjun Wang, Ling Wong, Eleanor Li, Jingmei Tin, Aung Cheng, Ching-Yu Heng, Chew-Kiat Yuan, Jian-Min Koh, Woon-Puay Saw, Seang Mei Friedlander, Yechiel Sim, Xueling Chai, Jin Fang Chong, Yap Seng Davila, Sonia Goh, Liuh Ling Lee, Eng Sing Wong, Tien Yin Karnani, Neerja Leong, Khai Pang Yeo, Khung Keong Chambers, John C. Lim, Su Chi Goh, Rick Siow Mong Tan, Patrick Dorajoo, Rajkumar Genome Institute of Singapore Agency for Science Technology and Research Singapore Singapore Eye Research institute Singapore National Eye Centre Singapore Yong Loo Lin School of Medicine National University of Singapore Singapore Duke-NUS Medical School Singapore Singapore National Eye Centre Singapore Department of Paediatrics Yong Loo Lin School of Medicine National University of Singapore Singapore Khoo Teck Puat - National University Children's Medical Institute National University Health System Singapore Department of Epidemiology Graduate School of Public Health University of Pittsburgh PittsburghPA United States UPMC Hillman Cancer Center University of Pittsburgh PittsburghPA United States Healthy Longevity Translational Research Programme Yong Loo Lin School of Medicine National University of Singapore Singapore Singapore Institute for Clinical Sciences Agency for Science Technology and Research Singapore Saw Swee Hock School of Public Health National University of Singapore Singapore Braun School of Public Health Hebrew University of Jerusalem Jerusalem Israel Precision Health Research Singapore SingHealth Duke-NUS Institute of Precision Medicine Singapore Molecular Diagnostic Laboratory Tan Tock Seng Hospital Singapore Clinical Research Unit National Healthcare Group Polyclinics Singapore Lee Kong Chian School of Medicine Nanyang Technological University Singapore Bioinformatics Institute Agency for Science Technology and Research Singapore Personalised Medicine Service Tan Tock Seng Hospital Singapore Department of Cardiology National Heart Centre Singapore Singapore School of Public Health Imperial College London United Kingdom Clinical Research Unit Khoo Teck Puat Hospital Singapore Diabetes Centre Admiralty Medical Centre Singapore Institute of High Performance Computing Agency for Science Technology and Research Singapore Cancer Science Institute of Singapore National University of Singapore Singapore

Genomic researchers are increasingly utilizing commercial cloud platforms (CCPs) to manage their data and analytics needs. Commercial clouds allow researchers to grow their storage and analytics capacity on demand, keeping pace with expanding project data footprints and enabling researchers to avoid large capital expenditures while paying only for IT capacity consumed by their project. Cloud computing also allows researchers to overcome common network and storage bottlenecks encountered when combining or re-analysing large datasets. However, cloud computing presents a new set of challenges. Without adequate security controls, the risk of unauthorised access may be higher for data stored on the cloud. In addition, regulators are increasingly mandating data access patterns and specific security protocols on the storage and use of genomic data to safeguard rights of the study participants. While CCPs provide tools for security and regulatory compliance, utilising these tools to build the necessary controls required for cloud solutions is not trivial as such skill sets are not commonly found in a genomics lab. The Research Assets Provisioning and Tracking Online Repository (RAPTOR) by the Genome Institute of Singapore is a cloud native genomics data repository and analytics platform focusing on security and regulatory compliance. Using a "five-safes" framework (Safe Purpose, Safe People, Safe Settings, Safe Data and Safe Output), RAPTOR provides security and governance controls to data contributors and users leveraging cloud computing for sharing and analysis of large genomic datasets without the risk of security breaches or running afoul of regulations. RAPTOR can also enable data federation with other genomic data repositories using GA4GH community-defined standards, allowing researchers to boost the statistical power of their work and overcome geographic and ancestry limitations of data sets © 2022, The Authors. All rights reserved.

关键词： Genome

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BENCHIP： Benchmarking Intelligence Processors

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Journal of Computer Science & technology 2018年第1期33卷 1-23页

作者： Jin-Hua Tao Zi-Dong Du Qi Guo Hui-Ying Lan Lei Zhang Sheng-Yuan Zhou Ling-Jie Xu Cong Liu Hai-Feng Liu Shah Tang Allen Rush Willian Chen Shao-Li Liu Yun-Ji Chen Tian-Shi Chen State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing 100049 China Intelligent Processor Research Center Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China Cambricon Ltd. Beijing 100190 China A libaba Infrastructure Service A libaba Group Hangzhou 311121 China Iflytek Co. Ltd. Hefei 230088 China Beijing Jingdong Century Trading Co. Ltd. Beijing 100176 China RDA Microdectronics Inc. Shanghai 201203 China Advanced Micro Devices Inc. Sunnyvale CA 94085 U.S.A.

The increasing attention on deep learning has tremendously spurred the design of intelligence processing hardware. The variety of emerging intelligence processors requires standard benchmarks for fair comparison and system optimization （in both software and hardware）. However, existing benchmarks are unsuitable for benchmarking intelligence processors due to their non-diversity and nonrepresentativeness. Also, the lack of a standard benchmarking methodology further exacerbates this problem. In this paper, we propose BENCHIP, a benchmark suite and benchmarking methodology for intelligence processors. The benchmark suite in BENCHIP consists of two sets of benchmarks： microbenchmarks and macrobenchmarks. The microbenchmarks consist of single-layer networks, They are mainly designed for bottleneck analysis and system optimization. The macrobenchmarks contain state-of-the-art industrial networks, so as to offer a realistic comparison of different platforms. We also propose a standard benchmarking methodology built upon an industrial software stack and evaluation metrics that comprehensively reflect various characteristics of the evaluated intelligence processors, BENCHIP is utilized for evaluating various hardware platforms, including CPUs, GPUs, and accelerators. BENCHIP will be open-sourced soon.

关键词： deep learning intelligence processor benchmark

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An Improved Convolutional Neural Network Algorithm for Multi-Label Classification

An Improved Convolutional Neural Network Algorithm for Multi...

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International Conference on Audio, Language and Image Processing, ICALIP

作者： Xinsheng Wang Lijun Sun Zhihua Wei Department of Computer Science and Technology Tongji University Shanghai China Research Center of Big Data and Network Security Tongji University Shanghai China Key Laboratory of Embedded System and Service Computing Tongji University Shanghai China

Recent years conventional neural network(CNN) has been applied to different natural language processing(NLP) tasks such as sentence classification, sentence modeling, etc. Some researchers use CNN to do multi-label classification but their work mainly focus on image rather than text. In this paper, we propose an improved CNN via hierarchical dirichlet process(HDP) model to deal with the multi-label classification problem in NLP. We first apply an HDP model to discard some words which are less important semantically. Then we use word embedding methods to transform words to vectors. Finally, we train CNN based on word vectors. Experimental results demonstrate that our method is superior to most traditional multi-label classification methods and TextCNN in terms of performance.

关键词： Feature extraction Training Convolutional neural networks Natural language processing Vocabulary Transforms

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Implicit multi-feature learning for dynamic time series prediction of the impact of institutions

arXiv

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arXiv 2020年

作者： Bai, Xiaomei Zhang, Fuli Hou, Jie Xia, Feng Tolba, Amr Elashkar, Elsayed Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province School of Software Dalian University of Technology Dalian116620 China Computing Center Anshan Normal University Anshan114007 China Library Anshan Normal University Anshan114007 China Computer Science Department Community College King Saud University Riyadh11437 Saudi Arabia Mathematics Department Faculty of Science Menoufia University Shebin El-Kom32511 Egypt Administrative Sciences Department Community College King Saud University Riyadh11437 Saudi Arabia Applied Statistics Department Faculty of Commerce Mansoura University Egypt

Predicting the impact of research institutions is an important tool for decision makers, such as resource allocation for funding bodies. Despite significant effort of adopting quantitative indicators to measure the impact of research institutions, little is known that how the impact of institutions evolves in time. Previous researches have focused on using the historical relevance scores of different institutions to predict potential future impact for these institutions. In this paper, we explore the factors that can drive the changes of the impact of institutions, finding that the impact of an institution, as measured by the number of the accepted papers of the institution, more is determined by the authors’ influence of the institution. Geographic location of institution feature and state GDP can drive the changes of the impact of institutions. Identifying these features allows us to formulate a predictive model that integrates the effects of individual ability, location of institution, and state GDP. The model unveils the underlying factors driving the future impact of institutions, which can be used to accurately predict the future impact of institutions. Copyright © 2020, The Authors. All rights reserved.

关键词： Forecasting

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A novel visible-infrared image fusion framework for smart city

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International Journal of Simulation and Process Modelling 2018年第2期13卷 144-155页

作者： Zhu, Zhiqin Qi, Guanqiu Chai, Yi Yin, Hongpeng Sun, Jian School of Automation Chongqing University Chongqing400044 China State Key Laboratory of Power Transmission Equipment and System Security and New Technology College of Automation Chongqing University Chongqing400044 China School of Computing Informatics and Decision Systems Engineering Arizona State University TempeAZ85287 United States Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education Chongqing400030 China

Image fusion technology is widely used in different areas and can integrate complementary and relevant information of source images captured by multiple sensors into a unitary synthetic image. Image fusion technology as an efficient way to integrate information from multiple images plays a more and more important role in smart city. The quality of fused image affects the accuracy, efficiency, and robustness of the related applications. Existing sparse representation-based image fusion methods consist of overly complete and redundant dictionaries learning and sparse coding. However, overly complete and redundant dictionary does not consider the discriminative ability of dictionaries that may seriously affect the image fusion. A good dictionary is the key to a successful image fusion technique. To construct a discriminative dictionary, a novel framework that integrates an image-patches clustering and online dictionary learning methods is proposed for visible-infrared image fusion. The comparison experiments with existing solutions are used to validate and demonstrate the effectiveness of the proposed solution for image fusion. Copyright © 2018 Inderscience Enterprises Ltd.

关键词： Image fusion

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Spike Sorting Based on Window-Gradient Feature

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Journal of Physics: Conference Series 2019年第2期1176卷

作者： Min Dai Feng Jia Wen Yang Key Laboratory of Computer Vision and System Ministry of Education Tianjin University of Technology 300384 Tianjin China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin University of Technology Tianjin 300384 China

Spike sorting is difficult when there is high waveforms similarity between different spike or when there is a large number of superimposed spikes in the sample. A new sample optimization method is proposed in the paper, called window-gradient feature. Every spike waveform is segmented into successive fragments in terms of the width σ, and each segment is called a window. Then calculate the gradient change of each window and use the ratio as the new alternative feature of the window. Finally, all window-gradient features of the spike waveforms are used to replace the original waveform features for spike sporting. The method is verified on the simulation data at different SNR. The experimental results show that when using SVM for spike sorting, the optimization effect of the proposed method is better than PCA, especially for data sets data sets with high noise or large sampling waveform similarity.

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