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arXiv 2017年

作者： Liu, Feng Shi, Yong Liu, Ying Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China College of Information Science and Technology University of Nebraska at Omaha OmahaNE68182 United States School of Economics and Management University of Chinese Academy of Sciences Beijing100190

The rapid development of artificial intelligence has brought the artificial intelligence threat theory as well as the problem about how to evaluate the intelligence level of intelligent products. Both need to find a quantitative method to evaluate the intelligence level of intelligence systems, including human intelligence. Based on the standard intelligence system and the extended Von Neumann architecture, this paper proposes General IQ, Service IQ and Value IQ evaluation methods for intelligence systems, depending on different evaluation purposes. Among them, the General IQ of intelligence systems is to answer the question of whether "the artificial intelligence can surpass the human intelligence", which is reflected in putting the intelligence systems on an equal status and conducting the unified evaluation. The Service IQ and Value IQ of intelligence systems are used to answer the question of "how the intelligent products can better serve the human", reflecting the intelligence and required cost of each intelligence system as a product in the process of serving human. Copyright © 2017, The Authors. All rights reserved.

关键词： Testing

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Research on knowledge Sharing and Transfer in Remanufacturing Engineering management Based on SECI Model

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Frontiers of Engineering management 2016年第2期3卷 136-143页

作者： Ling-ling Zhang Ming-hui Zhao Qiao Wang School of Economics and Management University of Chinese Academy of Sciences Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences

In this paper, an application mode and method of knowledge management in remanufacturing engineering management is established based on Nonaka's SECI model. The relationships between knowledge transfer,knowledge sharing and remanufacturing engineering management are highlighted. It is noticeable that a great deal of knowledge transfer and sharing activities, which can improve the performance of remanufacturing engineering management constantly, are involved in remanufacturing engineering.

关键词： remanufacturing engineering management knowledge transfer and sharing knowledge management

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AN EXTENDED MODEL FOR PROJECT PORTFOLIO SELECTION WITH PROJECT DIVISIBILITY AND INTERDEPENDENCY

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Journal of Systems Science and Systems Engineering 2016年第1期25卷 119-138页

作者： Xingmei Li Shu-Cherng Fang Xiaoling Guo Zhibin Deng Jianxun Qi School of Economics and Management North China Electric Power University Beijing 102206 China Industrial and Systems Engineering Department North Carolina State University Raleigh NC 27695 USA College of Engineering andlnformation Technology University of Chinese Academy of Sciences Beijing100049 China School of Management University of Chinese Academy of Sciences Beijing 100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing 100190 China

In this paper, we develop an extended model for the project portfolio selection problem over a planning horizon with multiple time periods. The model incorporates the factors of project divisibility and interdependency at the same time for real-life applications. The project divisibility is considered as a strategy, not an unfortunate event as in the literature, in choosing the best execution schedule for the projects, and the classical concept of＂project interdependencies＂ among fully executed projects is then extended to the portions of executed projects. Additional constraints of reinvestment consideration, setup cost, cardinality restriction, precedence relationship and scheduling are also included in the model. For efficient computations, an equivalent mixed integer linear programming representation of the proposed model is derived. Numerical examples under four scenarios are presented to highlight the characteristics of the proposed model. In particular, the positive effects of project divisibility are shown for the first time.

关键词： Project management project portfolio selection problem project divisibility project interdependency

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Inverse extreme learning machine for learning with label proportions

Inverse extreme learning machine for learning with label pro...

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IEEE International Conference on big data

作者： Limeng Cui Jiawei Zhang Zhensong Chen Yong Shi Philip S. Yu School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing China Department of Computer Science Florida State University FL USA Chinese Academy of Sciences Key Laboratory of Big Data Mining and Knowledge Management Beijing China Department of Computer Science University of Illinois at Chicago IL USA

In large-scale learning problem, the scalability of learning algorithms is usually the key factor affecting the algorithm practical performance, which is determined by both the time complexity of the learning algorithms and the amount of supervision information (i.e., labeled data). Learning with label proportions (LLP) is a new kind of machine learning problem which has drawn much attention in recent years. Different from the well-known supervised learning, LLP can estimate a classifier from groups of weakly labeled data, where only the positive/negative class proportions of each group are known. Due to its weak requirements for the input data, LLP presents a variety of real-world applications in almost all the fields involving anonymous data, like computer vision, fraud detection and spam filtering. However, even through the required labeled data is of a very small amount, LLP still suffers from the long execution time a lot due to the high time complexity of the learning algorithm itself. In this paper, we propose a very fast learning method based on inversing output scaling process and extreme learning machine, namely Inverse Extreme Learning Machine (IELM), to address the above issues. IELM can speed up the training process by order of magnitudes for large datasets, while achieving highly competitive classification accuracy with the existing methods at the same time. Extensive experiments demonstrate the significant speedup of the proposed method. We also demonstrate the feasibility of IELM with a case study in real-world setting: modeling image attributes based on ImageNet Object Attributes dataset.

关键词： Training Calibration Support vector machines Supervised learning Visualization Optimization

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The Regime Characteristics of Chinese Stock Market Industry Sectors

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Procedia Computer Science 2016年 91卷 512-518页

作者： Jiangjian Shen Wen Long CAS Research Center On Ficititious Economy & Data Science BeiJing 100190 Chian School of Economics and Management UCAS BeiJing 100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences BeiJing 100190 China

This article uses Markov regime Switching ARCH (SWARCH) model to research the volatility of Chinese stock market industry sectors, finding that all industry sectors were able to be significantly divided into two regimes, the high volatility regime and the low volatility regime. For different regime transfer, we can classify all sectors into three categories. Further the article analyzes the regime characteristics of industry sectors. The results show that the correlation coefficient in high volatility regime is higher than that in low volatility regime.

关键词： Industry sectors High volatility regime Low volatility regime Correlation

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Tri-level combination for image representation 17th

Tri-level combination for image representation

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17th Pacific-Rim Conference on Multimedia, PCM 2016

作者： Li, Ruiying Zhang, Chunjie Huang, Qingming School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783319488899

The context of objects can provide auxiliary discrimination beyond objects. However, this effective information has not been fully explored. In this paper, we propose Tri-level Combination for Image Representation (TriCoIR) to solve the problem at three different levels: object intrinsic, strongly-related context and weakly-related context. Object intrinsic excludes external disturbances and more focuses on the objects themselves. Strongly-related context is cropped from the input image with a more loose bound to contain surrounding context. Weaklyrelated one is recovered from the image other than object for global context. First, strongly and weakly-related context are constructed from input images. Second, we make cascade transformations for more intrinsical object information, which depends on the consistency between generated global context and input images in the regions other than object. Finally, a joint representation is acquired based on these three level features. The experiments on two benchmark datasets prove the effectiveness of TriCoIR. © Springer International Publishing AG 2016.

关键词： Image representation

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Exploring outliers in crowdsourced ranking for QoE

arXiv

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arXiv 2017年

作者： Xu, Qianqian Yan, Ming Huang, Chendi Xiong, Jiechao Huang, Qingming Yao, Yuan Institute of Information Engineering Cas Beijing100093 Department of Computational Mathematics Michigan State University East LansingMI48824 United States BICMR-LMAM-LMEQF-LMP School of Mathematical Sciences Peking University Beijing100871 Tencent Ai Lab Shenzhen518057 University of Chinese Academy of Sciences Beijing100049 China Key Lab of Intell. Info. Process. Inst. of Comput. Tech. Cas Beijing100190 Key Lab of Big Data Mining and Knowledge Management Cas Beijing100190 Department of Mathematics Hong Kong University of Science and Technology 100871 Hong Kong

Outlier detection is a crucial part of robust evaluation for crowd-sourceable assessment of Quality of Experience (QoE) and has attracted much attention in recent years. In this paper, we propose some simple and fast algorithms for outlier detection and robust QoE evaluation based on the nonconvex optimization principle. Several iterative procedures are designed with or without knowing the number of outliers in samples. Theoretical analysis is given to show that such procedures can reach statistically good estimates under mild conditions. Finally, experimental results with simulated and real-world crowdsourcing datasets show that the proposed algorithms could produce similar performance to Huber-LASSO approach in robust ranking, yet with nearly 8 or 90 times speed-up, without or with a prior knowledge on the sparsity size of outliers, respectively. Therefore the proposed methodology provides us a set of helpful tools for robust QoE evaluation with crowdsourcing data. Copyright © 2017, The Authors. All rights reserved.

关键词： Crowdsourcing

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Learning compact binary quantization of Minutia Cylinder Code

Learning compact binary quantization of Minutia Cylinder Cod...

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IAPR International Conference on Biometrics (ICB)

作者： Chaochao Bai Weiqiang Wang Tong Zhao Mingqiang Li School of Computer & Control University of Chinese Academy of Sciences(CAS) Beijing China CAS Key Laboratory of Big Data Mining and Knowledge Management Beijing China School of Mathematical Sciences University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781509018703

With explosive growth in fingerprint database, Automatic Fingerprint Identification System (AFIS) has become more difficult than ever. Consequently, it is necessary to get an effective and discriminative fingerprint feature binary representation. In this paper, we firstly analyze the characteristic of Minutia Cylinder Code (MCC) representation to find that it is strongly bit-correlated and with a lossy binary quantization. Accordingly, we propose an optimization model to learn a feature projection matrix resulting in dimensionality reduction as well as diminishing quantization loss. Eventually, the real-valued version of MCC is learnt to get Compact Binary Minutia Cylinder Code (CBMCC) with balanced independent property and minimal binary quantization loss. The performance test shows that CBMCC is effective and discriminative as it has maximum intra-bit variance while minimum inter-bit correlation. Furthermore, numerous experiments on public databases demonstrate that CBMCC is advantageous for fingerprint retrieval since it achieves a high correct index performance with a fairly low penetration rate.

关键词： Quantization (signal) Binary codes databases Mathematical model Optimization Correlation Fingerprint recognition

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A novel fingerprint classification method based on deep learning

A novel fingerprint classification method based on deep lear...

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International Conference on Pattern Recognition

作者： Ruxin Wang Congying Han Tiande Guo School of Mathematical Science University of Chinese Academy of Sciences (UCAS) Beijing School of Mathematical Science Key Laboratory of Big Data Mining and Knowledge Management UCAS

ISBN: (纸本)9781509048489

Fingerprint classification is an effective technique for reducing the candidate numbers of fingerprints in the stage of matching in automatic fingerprint identification system (AFIS). In recent years, deep learning is an emerging technology which has achieved great success in many fields, such as image processing, computer vision. In this paper, we have a preliminary attempt on the traditional fingerprint classification problem based on the new depth neural network method. For the four-class problem, only choosing orientation field as the classification feature, we achieve 91.4% accuracy using the stacked sparse autoencoders (SAE) with three hidden layers in the NIST-DB4 database. And then two classification probabilities are used for fuzzy classification which can effectively enhance the accuracy of classification. By only adjusting the probability threshold, we get the accuracy of classification is 96.1% (setting threshold is 0.85), 97.2% (setting threshold is 0.90) and 98.0% (setting threshold is 0.95) with a single layer SAE. Applying the fuzzy method, we obtain higher accuracy.

关键词： Machine learning databases Feature extraction Training Fingerprint recognition Neural networks Image reconstruction

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Genre-based Link Prediction in Bipartite Graph for Music Recommendation

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Procedia Computer Science 2016年 91卷 959-965页

作者： Daozhen Zhao Lingling Zhang Weiqi Zhao School of Economics and Management University of Chinese Academy of Sciences Beijing 100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing 100190 China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences BeiJing 100190 China

The bipartite graph method of link prediction can apply in many fields of recommendations, with the nodes (users and items) and links (interactions between users and items). However, that links cannot represent the users’ dual preferences (like and dislike). Some researchers improved that limits by complex number representations, but still not consider the influence of users’ similarity recommendation performance. Here, we proposed an improved method to cope with this deficiency, build the relational dualities by complex number representations and computing the users’ similarity by genres weight relations. In experiments with the *** music dataset, the proposed music genre weight-based music recommendation model (MGW) performances better than the CORLP method.

关键词： Music recommendation Link prediction Music genres

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