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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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Matrix Sketching Over Sliding Windows 16

Matrix Sketching Over Sliding Windows

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ACM SIGMOD International Conference on management of data

作者： Wei, Zhewei Liu, Xuancheng Li, Feifei Shang, Shuo Du, Xiaoyong Wen, Ji-Rong Renmin Univ China Sch Informat Key Lab Data Engn & Knowledge Engn MOEBeijing Key Lab Big Data Management & Anal Me Beijing Peoples R China Univ Utah Sch Comp Salt Lake City UT 84112 USA

ISBN: (纸本)9781450335317

Large-scale matrix computation becomes essential for many data data applications, and hence the problem of sketching matrix with small space and high precision has received extensive study for the past few years. This problem is often considered in the row-update streaming model, where the data set is a matrix A E R'd, and the processor receives a row (1 x d) of A at each timestamp. The goal is to maintain a smaller matrix (termed approximation matrix, or simply approximation) B E Rex d as an approximation to A, such that the covariance error MATA BT B11 is small and < n. This paper studies continuous tracking approximations to the matrix defined by a sliding window of most recent rows. We consider both sequence-based and time-based window. We show that maintaining ATA exactly requires linear space in the sliding window model, as opposed to 0(d2) space in the streaming model. With this observation, we present three general frameworks for matrix sketching on sliding windows. The sampling techniques give random samples of the rows in the window according to their squared norms. The Logarithmic Method converts a mergeable streaming matrix sketch into a matrix sketch on time-based sliding windows. The Dyadic Interval framework converts arbitrary streaming matrix sketch into a matrix sketch on sequence based sliding windows. In addition to proving all algorithmic properties theoretically, we also conduct extensive empirical study with real data sets to demonstrate the efficiency of these algorithms.

关键词： matrix sketching sliding window

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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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Computing the intersections of three conics according to their Jacobian curve

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JOURNAL OF SYMBOLIC COMPUTATION 2016年 73卷 175-191页

作者： Feng, Ruyong Shen, Li-Yong Chinese Acad Sci AMSS Key Lab Math Mech Beijing 100864 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 100864 Peoples R China

In this study, we describe the intersection of three conics based on the singularities of their corresponding Jacobian curve. In particular, we show that certain singular points and sub-lines of the Jacobian curve are the precise common points and common tangent lines of the conics, respectively. Based on our results, these points or the tangent line can be computed as the singularities of the Jacobian curve. These results facilitate investigations of the relationships between a net of conics and their Jacobian curve. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Algebraic condition Conic Intersection Jacobian curve Singularity

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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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The Regime Characteristics of Chinese Stock Market Industry Sectors

The Regime Characteristics of Chinese Stock Market Industry ...

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4th International Conference on Information Technology and Quantitative management (ITQM) - Promoting Business Analytics and Quantitative management of Technology

作者： Shen, Jiangjian Long, Wen Chinese Acad Sci Res Ctr Ficititious Econ & Data Sci Beijing 100190 Peoples R China UCAS Sch Econ & Management Beijing 100190 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R 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. (C) 2016 Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license.

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

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Unsupervised feature learning from time series 25

Unsupervised feature learning from time series

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25th International Joint Conference on Artificial Intelligence, IJCAI 2016

作者： Qin, Zhang Wu, Jia Hong, Yang Tian, Yingjie Chengqi, Zhang Quantum Computation and Intelligent Systems Centre University of Technology Sydney Australia Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing China Key Lab of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China MathWorks Beijing China

In this paper we study the problem of learning discriminative features (segments), often referred to as shapelets [Ye and Keogh, 2009] of time series, from unlabeled time series data. Discovering shapelets for time series classification has been widely studied, where many search-based algorithms are proposed to efficiently scan and select segments from a pool of candidates. However, such types of search-based algorithms may incur high time cost when the segment candidate pool is large. Alternatively, a recent work [Grabocka et al., 2014] uses regression learning to directly learn, instead of searching for, shapelets from time series. Motivated by the above observations, we propose a new Unsupervised Shapelet Learning Model (USLM) to efficiently learn shapelets from unlabeled time series data. The corresponding learning function integrates the strengths of pseudo-class label, spectral analysis, shapelets regularization term and regularized least-squares to auto-learn shapelets, pseudo-class labels and classification boundaries simultaneously. A coordinate descent algorithm is used to iteratively solve the learning function. Experiments show that USLM outperforms searchbased algorithms on real-world time series data.

关键词： Time series

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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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Geological Disaster Recognition on Optical Remote Sensing Images Using Deep Learning

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Procedia Computer Science 2016年 91卷 566-575页

作者： Ying Liu Linzhi Wu University of Chinese Academy of Sciences Beijing 100190 China Key Lab of Big Data Mining & Knowledge Management Chinese Academy of Sciences Beijing 100190 China

Geological disaster recognition, especially, landslide recognition, is of vital importance in disaster prevention, disaster monitoring and other applications. As more and more optical remote sensing images are available in recent years, landslide recognition on optical remote sensing images is in demand. Therefore, in this paper, we propose a deep learning based landslide recognition method for optical remote sensing images. In order to capture more distinct features hidden in landslide images, a particular wavelet transformation is proposed to be used as the preprocessing method. Next, a corrupting & denoising method is proposed to enhance the robustness of the model in recognize landslide features. Then, a deep auto-encoder network with multiple hidden layers is proposed to learn the high-level features and representations of each image. A softmax classifier is used for class prediction. Experiments are conducted on the remote sensing images from Google Earth. The experimental results indicate that the proposed wav DAE method outperforms the state-of-the-art classifiers both in efficiency and accuracy.

关键词： target recognition deep learning remote sensing image

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Scale Expansion and Operations Inefficiency in Pure E-commerce Firms

Scale Expansion and Operations Inefficiency in Pure E-commer...

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IEEE/WIC/ACM International Conference on Web Intelligence (WI)

作者： Yang, Zhuofan Shi, Yong Chinese Acad Sci Management Sch Univ Chinese Acad Sci Res Ctr Fictitious Econ & Data Sci Beijing Peoples R China Chinese Acad Sci Res Ctr Fictitious Econ & Data Sci Key Res Lab Big Data Min & Knowledge Management Beijing Peoples R China

ISBN: (纸本)9781509047710

This paper examine the importance of website quality indicator in e-commerce efficiency evaluation. It applies data Envelopment Analysis (DEA) models and traditional production theory to account for how scale affects efficiency in pure e-commerce firms. The results of DEA models identify website quality, user scale and user experience each contribute to improving operations efficiency, but scale expansion often leads to overall operations inefficiency. Due to excessive scale expansion, all inefficient Decision Making Units exhibit decreasing return to scale. These findings may help managers resize the scale of their operations. This research enriches scale economic theory and improves the indicator system in e-commerce efficiency evaluation.

关键词： data envelopment analysis e-commerce efficiency returns to scale

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