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Culture versus Policy: More Global Collaboration to Effectively Combat COVID-19

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The Innovation 2020年第2期1卷 15-16页

作者： Jianping Li Kun Guo Enrique Herrera Viedma Heesoek Lee Jiming Liu Ning Zhong Luiz Flavio Autran Monteiro Gomes Florin Gheorghe Filip Shu-Cherng Fang Mujgan SagirÖzdemir Xiaohui Liu Guoqing Lu Yong Shi School of Economics and Management University of Chinese Academy of SciencesBeijing 100190China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of SciencesBeijing 100190China Research Center on Fictitious Economy&Data Science Chinese Academy of SciencesBeijing 100190China Department of Computer Science and Artificial Intelligence E.T.S.de Ingenieria Informatica y de TelecomunicacionesUniversity of Granada18071 GranadaSpain Department of Information Management Korea Advanced Institute of Science and TechnologySeoul 207-43 Korea Department of Computer Science and HKBU-CSD&NIPD Joint Research Laboratory for Intelligent Disease Surveillance and Control Hong Kong Baptist UniversityHong KongChina Department of Life Science and Informatics Maebashi Institute of TechnologyMaebashi 371-0816Japan Ibmec University Center Av.Presidente Wilson118Office#111020030-020 Rio de JaneiroBrazil The Romanian Academy Bucharest010071Romania Industrial and Systems Engineering Department North Carolina State UniversityRaleighNC 27695USA Department of Industrial Engineering Eskisehir Osmangazi University26480 EskisehirTurkey Department of Computer Science Brunel University LondonLondonUB83PHUK Department of Biology and School of Interdisciplinary Informatics University of Nebraska at OmahaOmahaNE 68182USA College of Information Science and Technology University of Nebraska at OmahaOmahaNE 68182USA

The outbreak of COVID-19 seriously challenges every government with regard to capacity and management of public health systems facing the catastrophic *** and anti-epidemic policy do not necessarily conflict with each *** countries and governments should be more tolerant to each other in seeking cultural and political consensus to overcome this historically tragic pandemic together.

关键词： seeking overcome policy

Unsupervised Keyword Extraction Method Based on Chinese Patent Clustering

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Unsupervised Keyword Extraction Method Based on Chinese Pate...

IEEE International Conference on big knowledge (ICBK)

作者： Yuxin Xie Xuegang Hu Yuhong Zhang Shi Li Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology) Ministry of Education School of Computer Science and Information Engineering Hefei University of Technology Hefei China Intelligent Connected Vehicle Research Institute (JAC Technical Center) Anhui Jianghuai Automobile Group Corp.Ltd Hefei China

Recently, patent data analysis has attracted a lot of attention, and patent keyword extraction is a hot problem. Most existing methods for patent keyword extraction are based on the frequency of words without semantic information. In this paper, we propose an Unsupervised Keyword Extraction Method (UKEM) based on Chinese patent clustering. More specifically, we use a Skip-gram model to train word embeddings based on a Chinese patent corpus. Then each patent is represented as a vector called patent vector. These patent vectors are clustered to obtain several cluster centroids. Next, the distance between each word vector in patent abstract and cluster centroid is computed to indicate the semantic importance of this word. The experimental results on several Chinese patent datasets show that the performance of our proposed method is better than several competitive methods.

关键词： Patents Semantics data mining knowledge engineering Neural networks big data Computer science

Hierarchical Information Quadtree: Efficient Spatial Temporal Image Search for Multimedia Stream

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arXiv 2018年

作者： Zhang, Chengyuan Chen, Ruipeng Zhu, Lei Liu, Anfeng Lin, Yunwu Huang, Fang School of Information Science and Engineering Central South University China Big Data and Knowledge Engineering Institute Central South University China

Massive amount of multimedia data that contain times- tamps and geographical information are being generated at an unprecedented scale in many emerging applications such as photo sharing web site and social networks applications. Due to their importance, a large body of work has focused on efficiently computing various spatial image queries. In this paper,we study the spatial temporal image query which considers three important constraints during the search including time recency, spatial proximity and visual relevance. A novel index structure, namely Hierarchical Information Quadtree(HI-Quadtree), to efficiently insert/delete spatial temporal images with high arrive rates. Base on HI-Quadtree an efficient algorithm is developed to support spatial temporal image query. We show via extensive experimentation with real spatial databases clearly demonstrate the efficiency of our methods. Copyright © 2018, The Authors. All rights reserved.

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Efficient interactive search for geo-tagged multimedia data

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arXiv 2018年

作者： Long, Jun Zhu, Lei Zhang, Chengyuan Yang, Zhan Lin, Yunwu Chen, Ruipeng School of Information Science and Engineering Central South University China Big Data and Knowledge Engineering Institute Central South University China

Due to the advances in mobile computing and multimedia techniques, there are vast amount of multimedia data with geographical information collected in multifarious applications. In this paper, we propose a novel type of image search named interactive geo-tagged image search which aims to find out a set of images based on geographical proximity and similarity of visual content, as well as the preference of users. Existing approaches for spatial keyword query and geo-image query cannot address this problem effectively since they do not consider these three type of information together for query. In order to solve this challenge efficiently, we propose the definition of interactive top-k geo-tagged image query and then present a framework including candidate search stage , interaction stage and termination stage. To enhance the searching efficiency in a largescale database, we propose the candidate search algorithm named GI-SUPER Search based on a new notion called superior relationship and GIR-Tree, a novel index structure. Furthermore, two candidate selection methods are proposed for learning the preferences of the user during the interaction. At last, the termination procedure and estimation procedure are introduced in brief. Experimental evaluation on real multimedia dataset demonstrates that our solution has a really high performance. Copyright © 2018, The Authors. All rights reserved.

关键词： Query processing

Fully-convolutional intensive feature flow neural network for text recognition

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arXiv 2019年

作者： Zhang, Zhao Tang, Zemin Zhang, Zheng Wang, Yang Qin, Jie Wang, Meng School of Computer Science and Technology Soochow University China Key Laboratory of Knowledge Engineering with Big Data Ministry of Education School of Computer and Information Hefei University of Technology Hefei China Bio-Computing Research Center Harbin Institute of Technology Shenzhen518055 China Inception Institute of Artificial Intelligence Abu Dhabi United Arab Emirates

The Deep Convolutional Neural Networks (CNNs) have obtained a great success for pattern recognition, such as recognizing the texts in images. But existing CNNs based frameworks still have several drawbacks: 1) the traditaional pooling operation may lose important feature information and is unlearnable;2) the traditional convolution operation optimizes slowly and the hierarchical features from different layers are not fully utilized. In this work, we address these problems by developing a novel deep network model called Fully-Convolutional Intensive Feature Flow Neural Network (IntensiveNet). Specifically, we design a further dense block called intensive block to extract the feature information, where the original inputs and two dense blocks are connected tightly. To encode data appropriately, we present the concepts of dense fusion block and further dense fusion operations for our new intensive block. By adding short connections to different layers, the feature flow and coupling between layers are enhanced. We also replace the traditional convolution by depthwise separable convolution to make the operation efficient. To prevent important feature information being lost to a certain extent, we use a convolution operation with stride 2 to replace the original pooling operation in the customary transition layers. The recognition results on large-scale Chinese string and MNIST datasets show that our IntensiveNet can deliver enhanced recognition results, compared with other related deep models. Copyright © 2019, The Authors. All rights reserved.

关键词： Convolution

Temporal activity path based character correction in social networks

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arXiv 2018年

作者： Long, Jun Zhu, Lei Yang, Zhan Zhang, Chengyuan Yuan, Xinpan School of Information Science and Engineering Central South University Big Data and Knowledge Engineering Institute Central South University School of Computer Hunan University of Technology

Vast amount of multimedia data contains massive and multifarious social in-formation which is used to construct large-scale social networks. In a complex social network, a character should be ideally denoted by one and only one vertex. However, it is pervasive that a character is denoted by two or more vertices with different names, thus it is usually considered as multiple, different characters. This problem causes incorrectness of results in network analysis and mining. The factual challenge is that character unique-ness is hard to correctly confirm due to lots of complicated factors, e.g. name changing and anonymization, leading to character duplication. Early, limited research has shown that previous methods depended overly upon supplementary attribute information from databases. In this paper, we propose a novel method to merge the character vertices which refer to as the same entity but are denoted with different names. With this method, we firstly build the relationship network among characters based on records of social activities participated, which are extracted from multimedia sources. Then define temporal activity paths (TAPs) for each character over time. After that, we measure similarity of the TAPs for any two characters. If the similarity is high enough, the two vertices should be con-sidered to the same character. Based on TAPs, we can determine whether to merge the two character vertices. Our experiments shown that this solution can accurately confirm character uniqueness in large-scale social network. Copyright © 2018, The Authors. All rights reserved.

关键词： Machine learning

Learning Structured Twin-Incoherent Twin-Projective Latent Dictionary Pairs for Classification

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Learning Structured Twin-Incoherent Twin-Projective Latent D...

IEEE International Conference on data Mining (ICDM)

作者： Zhao Zhang Yulin Sun Zheng Zhang Yang Wang Guangcan Liu Meng Wang School of Computer Science and Technology Soochow University Suzhou China Key Laboratory of Knowledge Engineering with Big Data (Ministry of Education) Hefei University of Technology School of Computer Science and Information Engineering Hefei University of Technology Hefei China Bio-Computing Research Center Harbin Institute of Technology (Shenzhen) Shenzhen China School of Information and Control Nanjing University of Information Science and Technology Nanjing China

In this paper, we extend the popular dictionary pair learning (DPL) into the scenario of twin-projective latent flexible DPL under a structured twin-incoherence. Technically, a novel framework called Twin-Projective Latent Flexible DPL (TP-DPL) is proposed, which minimizes the twin-incoherence constrained flexibly-relaxed reconstruction error to avoid the possible over-fitting issue and produce accurate reconstruction. In this setting, TP-DPL integrates the twin-incoherence based latent flexible DPL and the joint embedding of codes as well as salient features by twin-projection into a unified model in an adaptive neighborhood-preserving manner. Therefore, TP-DPL can unify the procedures of salient feature representation and classification. The twin-incoherence constraint on coefficients and features can explicitly ensure high intra-class compactness and inter-class separation over them. TP-DPL also integrates the adaptive weighting to preserve local neighborhood of both coefficients and salient features within each class explicitly. For efficiency, TP-DPL selects the Frobenius-norm and abandons the costly l0/l1-norm for group sparse representation. Another byproduct is that TP-DPL can directly apply the class-specific twin-projective reconstruction residual to compute the label of data. Extensive results on public databases show that TP-DPL can deliver the state-of-the-art performance.

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A filter of minhash for image similarity measures

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arXiv 2018年

作者： Long, Jun Liu, Qunfeng Yuan, Xinpan Zhang, Chengyuan Liu, Junfeng School of Information Science and Engeering Central South University Big Data and Knowledge Engineering Institute Central South University School of Computer Hunan University of Technology

Image similarity measures play an important role in nearest neighbor search and duplicate detection for large-scale image datasets. Recently, Minwise Hashing (or Minhash) and its related hashing algorithms have achieved great performances in large-scale image retrieval systems. However, there are a large number of comparisons for image pairs in these applications, which may spend a lot of computation time and affect the performance. In order to quickly obtain the pairwise images that theirs similarities are higher than the specific threshold T (e.g., 0.5), we propose a dynamic threshold filter of Minwise Hashing for image similarity measures. It greatly reduces the calculation time by terminating the unnecessary comparisons in advance. We also find that the filter can be extended to other hashing algorithms, on when the estimator satisfies the binomial distribution, such as b-Bit Minwise Hashing, One Permutation Hashing, etc. In this pager, we use the Bag-of-Visual-Words (BoVW) model based on the Scale Invariant Feature Transform (SIFT) to represent the image features. We have proved that the filter is correct and effective through the experiment on real image datasets. Copyright © 2018, The Authors. All rights reserved.

关键词： Image analysis

An efficient approach for geo-multimedia cross-modal retrieval

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arXiv 2018年

作者： Zhu, Lei Long, Jun Zhang, Chengyuan Chen, Ruipeng Yuan, Xinpan Yang, Zhan School of Information Science and Engineering Central South University China Big Data and Knowledge Engineering Institute Central South University China School of Computer Hunan University of Technology China

Due to the rapid development of mobile Internet techniques, cloud computation and popularity of online social networking and location-based services, massive amount of multimedia data with geographical information is generated and uploaded to the Internet. In this paper, we propose a novel type of cross-modal multimedia retrieval called geo-multimedia cross-modal retrieval which aims to search out a set of geo-multimedia objects based on geographical distance proximity and semantic similarity between different modalities. Previous studies for cross-modal retrieval and spatial keyword search cannot address this problem effectively because they do not consider multimedia data with geo-tags and do not focus on this type of query. In order to address this problem efficiently, we present the deffinition of kNN geo-multimedia cross-modal query at the first time and introduce relevant conceptions such as cross-modal semantic representation space. To bridge the semantic gap between different modalities, we propose a method named cross-modal semantic matching which contains two important component, i.e., CorrProj and LogsTran, which aims to construct a common semantic representation space for cross-modal semantic similarity measurement. Besides, we designed a framework based on deep learning techniques to implement common semantic representation space construction. In addition, a novel hybrid indexing structure named GMR-Tree combining geo-multimedia data and R-Tree is presented and a efficient kNN search algorithm called kGMCMS is designed. Comprehensive experimental evaluation on real and synthetic dataset clearly demonstrates that our solution outperforms the-stateof-the-art methods. Copyright © 2018, The Authors. All rights reserved.

关键词： Location based services