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3rd IEEE International Conference on Computer and Communications (ICCC)

作者： Ge, Shishu Bai, Chaochao Liu, Yan Liu, Yonghong Zhao, Tong Univ Chinese Acad Sci Sch Comp & Control Engn Beijing Peoples R China Univ Chinese Acad Sci Sch Math Sci Beijing Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing Peoples R China

ISBN: (纸本)9781509063529

Fingerprint classification is still a challenging problem because of its large intra-class variance, small interclass variance and strong noise in the fingerprint patterns. Traditional methods usually utilize some human-defined features to classify fingerprints, however, these features are relatively shallow and local, and they cannot solve this problem well. In this paper, we propose a deep convolutional neural network called Res-FingerNet to solve this problem. The network can extract more abstract and global features from fingerprint images. Moreover, in order to reduce intra-class variance and enlarge inter-class variance of the fingerprints, we utilize center loss in the network training stage so that the learned deep features are more discriminative for fingerprint classification task, and our experimental results show that the classification accuracy increases about 1.5% by center loss. The classification method has been measured on the public fingerprint database NIST-DB4 and it achieves a classification accuracy of 97.9% which surpasses most of existing fingerprint classification methods.

关键词： fingerprint classification convolutional neural network Res-FingerNet center loss

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HC-Midware: A Middleware to Enable High Performance Communication System Simulation in Heterogeneous Cloud 2017

HC-Midware: A Middleware to Enable High Performance Communic...

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International Conference on Wireless Communications, Networking and Applications (WCNA)

作者： Wu, Tao Liu, Ying Liu, Yan-Du Acad Equipment Dept Photoelect Equipment Beijing Peoples R China Univ Chinese Acad Sci Sch Comp & Control Beijing Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing Peoples R China Acad Equipment Dept Grad Management Beijing Peoples R China

ISBN: (纸本)9781450353441

Cloud computing has become a popular high performance computing model where resources are provided as services over the Web(1). Users are starting to adopt the cloud model in communication applications. However, due to the complexity of parallel/cloud computing, it is difficult for average users to express a parallel computing paradigm for their applications in cloud. In order to isolate users from the complexity of parallel/cloud programming, a middleware to enable high performance communication system simulation, called HC-Midware, is proposed. It hides the details of MapReduce programming from users by automatically launching mappers through a set of user programming APIs. Directive-based parallelization scheme automatically "translates" a serial program into a SMP or Multi-core based parallel program. Heterogeneous computing resources can be invoked to conduct parallel execution by API-based scheme which highlights the adaptability of HC-Midware. A two-step scheduling scheme is proposed to maximize the throughput of the cloud system. We evaluate HC-Midware by executing three representative communication system simulation applications in a private cloud. Good scalability and adaptability were observed in the experimental results.

关键词： Cloud Computing Parallel Computing Communication System Simulation

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A generalized concept-cognitive learning: A machine learning viewpoint

arXiv

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arXiv 2018年

作者： Mi, Yunlong Shi, Yong Li, Jinhai School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing101408 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing100190 China College of Information Science and Technology University of Nebraska at Omaha NE68182 United States Faculty of Science Kunming University of Science and Technology Kunming650500 China

Concept-cognitive learning (CCL) is a hot topic in recent years, and it has attracted much attention from the communities of formal concept analysis, granular computing and cognitive computing. However, the relationship among cognitive computing (CC), concept-cognitive computing (CCC), CCL and concept-cognitive learning model (CCLM) is not clearly described. To this end, we first explain the relationship of CC, CCC, CCL and CCLM. Then, we propose a generalized concept-cognitive learning (GCCL) from the point of view of machine learning. Finally, experiments on some data sets are conducted to verify the feasibility of concept formation and concept-cognitive process of GCCL. Copyright © 2018, The Authors. All rights reserved.

关键词： Machine learning

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The Tourism-Specific Sentiment Vector Construction Based on Kernel Optimization Function 5

The Tourism-Specific Sentiment Vector Construction Based on ...

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5th International Conference on Information Technology and Quantitative management (ITQM)

作者： Zhu, Luyao Li, Wei Guo, Kun Shi, Yong Zheng, Yuanchun Univ Chinese Acad Sci Sch Econ & Management Beijing Peoples R China Chinese Acad Sci Fictitious Econ & Data Sci Res Ctr Beijing Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing Peoples R China Univ Chinese Acad Sci Sch Comp & Control Engn Beijing Peoples R China

Sentiment analysis in tourism domain has drawn much attention in past few years, which calls for more precise sentiment word embedding method. The article proposes a kernel optimization function for sentiment word embedding. And the method aims at integrating the semantic information, statistics information and sentiment information and maintains the similarity between sentiment words in terms of sentiment orientation. The experiment result shows that the optimal sentiment vectors successfully extract the features in terms of sentiment information and the difference between concretization and abstraction of a sentiment words. (C) 2017 The Authors. Published by Elsevier B.V.

关键词： kernel function sentiment vector word embedding sentiment analysis

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Analysis of the Relation between Artificial Intelligence and the Internet from the Perspective of Brain Science 5

Analysis of the Relation between Artificial Intelligence and...

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5th International Conference on Information Technology and Quantitative management (ITQM)

作者： Liu, Feng Shi, Yong Li, Peijia Chinese Acad Sci Res Ctr Fictitious Econ & Data Sci Beijing 100190 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China Univ Nebraska Coll Informat Sci & Technol Omaha NE 68182 USA Univ Chinese Acad Sci Sch Econ & Management Beijing 100190 Peoples R China

Artificial intelligence (AI) like deep learning, cloud AI computation has been advancing at a rapid pace since 2014. There is no doubt that the prosperity of AI is inseparable with the development of the Internet. However, there has been little attention to the link between AI and the internet. This paper explores them with brain insights mainly from four views:1) How is the general relation between artificial intelligence and Internet of Things, cloud computing, big data and Industrial Internet from the perspective of brain science. 2) Construction of a new AI system model with the Internet and brain science. (C) 2017 The Authors. Published by Elsevier B.V.

关键词： Internet's virtual brain big SNS AI model of the Internet-like brain

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Metric learning for multi-instance classification with collapsed bags

Metric learning for multi-instance classification with colla...

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International Joint Conference on Neural Networks (IJCNN)

作者： Dewei Li Dongkuan Xu Jingjing Tang Yingjie Tian Research Center on Fictitious Economy & Data Science Chinese Academy of Sciences Beijing China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781509061839

As a kind of popular problem in machine learning, multi-instance task has been researched by means of many classical methods, such as kNN, SVM, etc. For kNN classification, its performance on traditional task can be boosted by metric learning, which seeks for a data-dependent metric to make similar examples closer and separate dissimilar examples by a margin. It is a challenge to define distance between bags in multi-instance problem, let alone learning appropriate metric for the problem. In this paper, we propose a new approach for multi-instance classification, with the idea of metric learning embedded. A new kind of distance is used to measure the similarity between bags. To weaken redundant information from bags and reduce computation complexity, k-means method is implemented to get collapsed bags by replacing each instance with its corresponding cluster centroid. The aim of metric learning is to expand inter-class bag distance and shrink intra-class bag distance, leading to the construction of an optimization problem with maximal relative distance. Kernel function can be introduced into the model to extract nonlinear information from the inputs. Gradient descent is utilized to solve the problem effectively. Numerical experiments on both artificial datasets and benchmark datasets demonstrated that the method can obtain competitive performance comparative to kNN and the state-of-the-art method in multi-instance classification.

关键词： Measurement Feature extraction data mining Optimization Kernel Training Drugs

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Contextual Exemplar Classifier-Based Image Representation for Classification

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2017年第8期27卷 1691-1699页

作者： Zhang, Chunjie Huang, Qingming Tian, Qi Chinese Acad Sci Inst Automat Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Comp & Control Engn Beijing 100049 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China Chinese Acad Sci Key Lab Intelligent Informat Proc Inst Comp Technol Beijing 100190 Peoples R China Univ Texas San Antonio Dept Comp Sci San Antonio TX 78249 USA

The use of local features for image representation has become popular in recent years. Local features are often used in the bag-of-visual-words scheme. Although proven effective, this method still has two drawbacks. First, local regions from which local features are extracted are not discriminative enough for visual tasks. Hence, the combination of local features is necessary. Second, the semantic gap between visual features and human perception also hinders the performance. To address these two problems, in this paper, we propose a novel contextual exemplar classifier-based method for image representation and apply it for classification tasks. Each exemplar classifier is trained to separate one training image from the other images of different classes. We partition each image into a number of regions and use the responses of these exemplar classifiers as the image region's representation. The contextual relationship is then modeled using mixture Dirichlet distributions. A bilayer model is used to predict image classes with L-2 constraints. Experimental results on the Natural Scene, Caltech-101/256, Flower-17/102, and SUN-397 data sets show that the proposed method is able to outperform the state-of-the-art local feature-based methods for image classification.

关键词： Computer vision image processing pattern classification

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A Margin-based MLE for Crowdsourced Partial Ranking

arXiv

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arXiv 2018年

作者： Xu, Qianqian Xiong, Jiechao Sun, Xinwei Yang, Zhiyong Cao, Xiaochun Huang, Qingming Yao, Yuan Key Lab of Intell. Info. Process. Inst. of Comput. Tech. Cas Beijing100190 Tencent Ai Lab Shenzhen518057 School of Mathematical Sciences Peking University Beijing100871 China DeepWise Ai Lab Beijing100085 Inst. of Info. Engin. Cas Beijing100093 China University of Chinese Academy of Sciences Beijing100049 China Key Lab of Big Data Mining and Knowledge Management Cas Beijing100190 Department of Mathematics Hong Kong University of Science and Technology Hong Kong Hong Kong

A preference order or ranking aggregated from pairwise comparison data is commonly understood as a strict total order. However, in real-world scenarios, some items are intrinsically ambiguous in comparisons, which may very well be an inherent uncertainty of the data. In this case, the conventional total order ranking can not capture such uncertainty with mere global ranking or utility scores. In this paper, we are specifically interested in the recent surge in crowdsourcing applications to predict partial but more accurate (i.e., making less incorrect statements) orders rather than complete ones. To do so, we propose a novel framework to learn some probabilistic models of partial orders as a margin-based Maximum Likelihood Estimate (MLE) *** prove that the induced MLE is a joint convex optimization problem with respect to all the parameters, including the global ranking scores and margin parameter. Moreover, three kinds of generalized linear models are studied, including the basic uniform model, Bradley-Terry model, and Thurstone-Mosteller model, equipped with some theoretical analysis on FDR and Power control for the proposed methods. The validity of these models are supported by experiments with both simulated and real-world datasets, which shows that the proposed models exhibit improvements compared with traditional state-ofthe-art algorithms. Copyright © 2018, The Authors. All rights reserved.

关键词： Maximum likelihood estimation

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Growth of Green Total Factor Productivity and Its Determinants of Cities in China: A Spatial Econometric Approach

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EMERGING MARKETS FINANCE AND TRADE 2017年第9期53卷 2123-2140页

作者： Tao, Feng Zhang, Huiqin Hu, Yi Duncan, Andrew A. Jinan Univ Inst Ind Econ Guangzhou Guangdong Peoples R China Univ Chinese Acad Sci Sch Econ & Management Beijing 100190 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing Peoples R China Univ Calif Irvine Sch Social Sci Irvine CA 92717 USA

As pollution in Chinese cities worsens, there is renewed interest in promoting sustainable development across the country. This article introduces the global Malmquist-Luenberger productivity index (ML) to measure green total factor productivity (TFP) growth of 270 Chinese cities over the period 2003-2013. Empirical spatial econometrics is applied to analyze the spatial spillovers and the determinants of green TFP growth. Our results show that green TFP is a useful metric for measuring development, and that spatial spillover effects do exist among neighboring cities of similar development. Finally, the study provides some policy implications.

关键词： green TFP growth spatial spillovers the GML productivity index the spatial Durbin model

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Image classification by search with explicitly and implicitly semantic representations

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INFORMATION SCIENCES 2017年 376卷 125-135页

作者： Zhang, Chunjie Zhu, Guibo Huang, Qingming Tian, Qi Univ Chinese Acad Sci Sch Comp & Control Engn Beijing 100049 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100864 Peoples R China Chinese Acad Sci Inst Automat Res Ctr Brain Inspired Intelligence Beijing 100190 Peoples R China Chinese Acad Sci Inst Comp Technol Key Lab Intell Info Proc Beijing 100190 Peoples R China Univ Texas San Antonio Dept Comp Sci San Antonio TX 78249 USA

Image classification refers to the task of automatically classifying the categories of images based on the contents. This task is typically solved using visual features with the histogram based classification scheme. Although effective, this strategy has two drawbacks. On one hand, histogram based representation often disregards the object layout which is very important for classification. On the other hand, visual features are unable to fully separate different images due to the semantic gap. To solve these two problems, in this paper, we propose a novel image classification method by explicitly and implicitly representing the images with searching strategy. First, to make use of object layouts, we randomly select a number of regions and then use these regions for image representations. Second, we generate the explicitly semantic representations using a number of pre-learned semantic models. Third, we measure the visual similarities with the Internet images and use the text information for implicitly semantic representations. Since Internet images are contaminated with noise, the resulting representations only implicitly reflect the contents of images. Finally, both the explicitly and implicitly semantic representations are jointly modeled for image classifications by training bi-linear classifiers. We evaluate the effectiveness of the proposed image classification by search with explicitly and implicitly semantic representations method (EISR) on the Scene-15 dataset, the MIT-Indoor dataset, the UIUC-Sports dataset and the PASCAL VOC 2007 dataset. The experimental results prove the usefulness of the proposed method. (C) 2016 Elsevier Inc. All rights reserved.

关键词： Explicit representation Implicit representation Semantic modeling Image classification

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