检索结果-内蒙古大学图书馆

Generalized Association and Deep Understanding Method for Big Video data Processing

学校读者我要写书评

暂无评论

Generalized Association and Deep Understanding Method for Bi...

IEEE International Symposium on Parallel and Distributed Processing with Applications and IEEE International Conference on Ubiquitous computing and Communications (ISPA/IUCC)

作者： Lin Xu Wei Wu Wanghan Lv Dong Wang Science and Technology in Information System Engineering Lab Nanjing Research institute of Electronic Engineering Nanjing China School of Information Science and Engineering University of Jinan Jinan China Shandong Provincial Key Laboratory of Network Based Intelligent Computing University of Jinan Jinan China

ISBN: (纸本)9781728152103

In Big data Era, it is becoming more and more important to timely and efficient processing of massive videos, and mining of the value information contained in them. This paper studies chaotic compressed sensing theory and uses Long-term and short-term memory networks (LSTM) algorithm to generate a summary of the content of video behavior track. It has already to apply the proposed method in target recognition and tracking, and the recognition rate achieves more than 86%.

关键词： Big data compressed sensing data compression data mining video coding big data Compressed sensing data compression Video coding data mining video data Target recognition

Learning knowledge representation across knowledge graphs 31

学校读者我要写书评

暂无评论

Learning knowledge representation across knowledge graphs

31st AAAI Conference on Artificial Intelligence, AAAI 2017

作者： Cai, Pengshan Li, Wei Feng, Yansong Wang, Yuanzhuo Jia, Yantao CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Science Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Science Beijing China Institute of Computer Science and Technology Peking University Beijing China

ISBN: (纸本)9781577357865

Distributed knowledge representation learning (KRL) methods encode both entities and relations in knowledge graphs (KG) in a lower-dimensional semantic space, which model relatively dense knowledge graphs well and greatly improve the performance of knowledge graph completion and knowledge reasoning. However, existing KRL methods including Trans(E, H, R, D and Sparse) hardly obtain comparative performances on sparse KGs where most of entities and relations have very low frequencies. Furthermore, all existing methods target at KRL on one knowledge graph independently. The embeddings of different KGs are independent with each other. In this paper, we propose a novel cross-knowledge- graph (cross-KG) KRL method which learns embeddings for two different KGs simultaneously. Through projecting semantic related entities and relations in two KGs to a uniform semantic space, our method could learn better embeddings for sparse KGs by incorporating information from another relatively larger and denser KG. The learned embeddings are also helpful for downstream cross-KGs or cross-linguals tasks like ontology alignment. The experiment results show that our method could significandy outperform corresponding baseline methods on knowledge graph completion on single KG and cross-KG entity prediction and mapping tasks. © 2017, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Knowledge graph

A data glove-based KEM dynamic gesture recognition algorithm

学校读者我要写书评

暂无评论

International Journal of Performability Engineering 2018年第11期14卷 2589-2600页

作者： Han, Rui Feng, Zhiquan Ai, Changsheng Xie, Wei Wang, Kang School of Information Science and Engineering University of Jinan Jinan250022 China Shandong Provincial Key Laboratory of Network Based Intelligent Computing Jinan250022 China School of Mechanical Engineering University of Jinan Jinan250022 China School of Information and Electrical Engineering Harbin Institute of Technology at Weihai WeiHai264209 China

data gloves-based gesture recognition plays a very important role in the virtual reality interaction system. A new dynamic gesture recognition method, that is, K-means clustering dimensionality reduction and Euclidean metric template matching algorithm based on data glove (KEM algorithm), is proposed in this paper. First, high-dimensional data is clustered in the K-means clustering algorithm to achieve dimensionality reduction. Then, the low-dimensional data is put into the template matching method based on Euclidean metric to get the distance that matches all the templates. Finally, the corresponding gesture is identified according to the template matching. The main innovations of the proposed KEM algorithm are as follows: (a) K-means clustering is applied to dynamic gesture recognition for the first time to achieve real-time recognition, (b) the classical K-means method is optimized, and (c) the template matching process is more reasonable. Experiments show that the proposed KEM method can achieve 99.42% in recognition rate. The validity of the KEM method has been verified in a 3D Intelligent Teaching System. © 2018 Totem Publisher, Inc. All rights reserved.

关键词： Template matching

Dual Graph-Laplacian PCA: A closed-form solution for bi-clustering to find "checkerboard" structures on gene expression data

学校读者我要写书评

暂无评论

arXiv 2019年

作者： Liu, Jin-Xing Feng, Chun-Mei Kong, Xiang-Zhen Xu, Yong School of Information Science and Engineering Qufu Normal University Rizhao Shandong276826 China Bio-Computing Research Center Shenzhen Graduate School Harbin Institute of Technology Key Laboratory of Network Oriented Intelligent Computation Shenzhen Guangdong518055 China

In the context of cancer, internal "checkerboard" structures are normally found in the matrices of gene expression data, which correspond to genes that are significantly up- or down-regulated in patients with specific types of tumors. In this paper, we propose a novel method, called dual graph-regularization principal component analysis (DGPCA). The main innovation of this method is that it simultaneously considers the internal geometric structures of the condition manifold and the gene manifold. Specifically, we obtain principal components (PCs) to represent the data and approximate the cluster membership indicators through Laplacian embedding. This new method is endowed with internal geometric structures, such as the condition manifold and gene manifold, which are both suitable for bi-clustering. A closed-form solution is provided for DGPCA. We apply this new method to simultaneously cluster genes and conditions (e.g., different samples) with the aim of finding internal "checkerboard" structures on gene expression data, if they exist. Then, we use this new method to identify regulatory genes under the particular conditions and to compare the results with those of other state-of-the-art PCA-based methods. Promising results on gene expression data have been verified by extensive experiments. Copyright © 2019, The Authors. All rights reserved.

关键词： Principal component analysis

Fine-grained event categorization with heterogeneous graph convolutional networks

学校读者我要写书评

暂无评论

arXiv 2019年

作者： Peng, Hao Li, Jianxin Gong, Qiran Song, Yangqiu Ning, Yuanxing Lai, Kunfeng Yu, Philip S. Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University State Key Laboratory of Software Development Environment Beihang University Department of Computer Science and Engineering Hong Kong University of Science and Technology Platform and Content Group Tencent Institute for Data Science Tsinghua University Department of Computer Science University of Illinois Chicago United States

Events are happening in real-world and real-time, which can be planned and organized occasions involving multiple people and objects. Social media platforms publish a lot of text messages containing public events with comprehensive topics. However, mining social events is challenging due to the heterogeneous event elements in texts and explicit and implicit social network structures. In this paper, we design an event meta-schema to characterize the semantic relatedness of social events and build an event-based heterogeneous information network (HIN) integrating information from external knowledge base, and propose a novel Pairwise Popularity Graph Convolutional network (PP-GCN) based fine-grained social event categorization model. We propose a Knowledgeable meta-paths Instances based social Event Similarity (KIES) between events and build a weighted adjacent matrix as input to the PP-GCN model. Comprehensive experiments on real data collections are conducted to compare various social event detection and clustering tasks. Experimental results demonstrate that our proposed framework outperforms other alternative social event categorization techniques. Copyright © 2019, The Authors. All rights reserved.

关键词： Information services

ICTNET at TREC 2017 Dynamic Domain Track 26

学校读者我要写书评

暂无评论

ICTNET at TREC 2017 Dynamic Domain Track

26th Text REtrieval Conference, TREC 2017

作者： Zhang, Weimin Hu, Yaokang Jia, Rongqian Wang, Xianfa Zhang, Le Feng, Yue Yu, Sihao Xue, Yuanhai Yu, Xiaoming Liu, Yue Cheng, Xueqi Institute of Computing Technology CAS China Key Laboratory of Web Data Science and Technology CAS China University of Chinese Academy of Sciences China

Constraining cosmological phase transitions with the Parkes Pulsar Timing Array

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Xue, Xiao Bian, Ligong Shu, Jing Yuan, Qiang Zhu, Xingjiang Bhat, N.D. Ramesh Dai, Shi Feng, Yi Goncharov, Boris Hobbs, George Howard, Eric Manchester, Richard N. Russell, Christopher J. Reardon, Daniel J. Shannon, R.M. Spiewak, Renée Thyagarajan, Nithyanandan Wang, Jingbo CAS Key Laboratory of Theoretical Physics Institute of Theoretical Physics Chinese Academy of Sciences Beijing100190 China School of Physical Sciences University of Chinese Academy of Sciences Beijing100049 China II. Institute of Theoretical Physics Universität Hamburg Hamburg22761 Germany Department of Physics Chongqing University Chongqing401331 China Chongqing Key Laboratory for Strongly Coupled Physics Chongqing401331 China CAS Key Laboratory of Theoretical Physics Insitute of Theoretical Physics Chinese Academy of Sciences Beijing100190 China CAS Center for Excellence in Particle Physics Beijing100049 China School of Fundamental Physics and Mathematical Sciences Hangzhou Institute for Advanced Study University of Chinese Academy of Sciences Hangzhou310024 China Center for High Energy Physics Peking University Beijing100871 China International Center for Theoretical Physics Asia-Pacific Hangzhou Beijing China Key Laboratory of Dark Matter and Space Astronomy Purple Mountain Observatory Chinese Academy of Sciences Nanjing210023 China School of Astronomy and Space Science University of Science and Technology of China Hefei230026 China School of Physics and Astronomy Monash University ClaytonVIC3800 Australia OzGrav: The ARC Centre of Excellence for Gravitational Wave Discovery HawthornVIC3122 Australia Advanced Institute of Natural Sciences Beijing Normal University Zhuhai519087 China International Centre for Radio Astronomy Research Curtin University BentleyWA6102 Australia Western Sydney University Locked Bag 1797 Penrith South DCNSW1797 Australia University of Chinese Academy of Sciences Beijing100049 China CSIRO Astronomy and Space Science P.O. Box 76 EppingNSW1710 Australia Macquarie University Department of Physics and Astronomy SydneyNSW2109 Australia CSIRO Scientific Computing Australian Technology Park Locked Bag 9013 AlexandriaNSW1435 Australia Centre for Astrophysics and Supercomputing Swinburne University of

A cosmological first-order phase transition is expected to produce a stochastic gravitational wave background. If the phase transition temperature is on the MeV scale, the power spectrum of the induced stochastic gravitational waves peaks around nanohertz frequencies, and can thus be probed with high-precision pulsar timing observations. We search for such a stochastic gravitational wave background with the latest data set of the Parkes Pulsar Timing Array. We find no evidence for a Hellings-Downs spatial correlation as expected for a stochastic gravitational wave background. Therefore, we present constraints on first-order phase transition model parameters. Our analysis shows that pulsar timing is particularly sensitive to the low-temperature (T ∼ 1 − 100 MeV) phase transition with a duration (β/H∗)−1 ∼ 10−2 − 10−1 and therefore can be used to constrain the dark and QCD phase transitions. © 2021, CC BY.

关键词： Pulsars

Vlbi data processing on coronal radio-sounding experiments of mars express

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Ma, Maoli Calvés, Guifré Molera Cimò, Giuseppe Zhang, Peijin Ming, Xiong Li, Peijia Kummamuru, Pradyumna Chu, Zhanghu Jiang, Tianyu Xia, Bo Tetsuro, Kondo Tong, Fengxian de Vicente, Pablo Quick, Jonathan Zhang, Hua Chen, Zhong Shanghai Astronomical Observatory 80 Nandan Road Shanghai China Shanghai Astronomical Observatory Key Laboratory of Radio Astronomy Chinese Academy of Sciences 10 Yuanhua Road JiangSu Nanjing210033 China University of Tasmania Private Bag 37 HobartTAS7001 Australia Joint Institute for VLBI ERIC Oude Hoogeveensedijk 4 PD Dwingeloo7991 Netherlands University of Science and Technology of China 96 Jinzhai Road Hefei China National Space Science Center NO.1 Nanertiao Zhongguancun Haidian district Beijing China Centro de Desarrollos Tecnológicos Observatorio de Yebes Guadalajara Spain Hartebeesthoek Radio Astronomy Observatory PO Box 443 Krugersdorp South Africa Xinjiang Astronomical Observatory CAS 150 Science 1-Street Urumqi China National Basic Science Data Center Building No.2 4 Zhongguancun South 4th Street Haidian District Beijing100190 China

The ESA's Mars Express solar corona experiments were performed at two solar conjunctions in the years 2015 and 2017 by a number of radio telescopes in the European VLBI network. This paper presents the methods to measure the frequency and phase fluctuations of the spacecraft radio signal, and the applications to study the characteristics of the plasma turbulence effects on the signal at a single station and at multiple stations via cross-correlation. The power spectra of the frequency fluctuations observed between 4.9 and 76.3 Rs have a power-law shape close to a Kolmogorov spectrum over the frequency interval νlo up, where the nominal value of νlo is set to 3 mHz and νup is in the range of 0.03 ∼ 0.15 Hz. The RMS of the frequency fluctuations is presented as a function of the heliocentric distance. Furthermore, we analyse the variations of the electron column density fluctuations at solar offsets 4.9 Rs and 9.9 Rs and the cross-correlation products between the VLBI stations. The power density of the differential fluctuations between different stations decreases at ν 700 km s−1 are derived from the cross-correlation of frequency fluctuations at ν Copyright © 2021, The Authors. All rights reserved.

关键词： Computational complexity

Design of Gesture Recognition System Based on Multi-Channel Myoelectricity Correlation

学校读者我要写书评

暂无评论

Design of Gesture Recognition System Based on Multi-Channel ...

2019 IEEE Global Communications Conference (GLOBECOM)

作者： Di Wu Huiyong Li Xuefeng Liu Jianwei Niu State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing China Beijing Advanced Innovation Center for Big Data and Brain Computing (BDBC) Beihang University Hangzhou Innovation Research Institute Beihang University

ISBN: (数字)9781728109626

ISBN: (纸本)9781728109633

Gesture recognition systems based on myoelectric signal have raised more and more attention from researchers. Traditional gesture recognition methods are susceptible to multiple types of noise and require a large number of features, which increase overhead and decrease recognition efficiency. Fully utilizing the characteristics of the signal to recognize gestures is a big challenge. This paper proposes an improved empirical mode decomposition method (XB-EMD) based on autocorrelation function to denoise myoelectric signal. In addition, a novel deep neural network (CRNet) which combines the CNN and RNN layers together is trained for classifying the gestures based on denoised myoelectric signal. Experimental results show that the proposed gesture recognition system can improve recognition effectiveness at 97.4\% with 10 typical gestures, and 72.55\% with 16 complicated gestures.

关键词： Correlation Gesture recognition Feature extraction Empirical mode decomposition Recurrent neural networks Sensors