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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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Strategy intervention for the evolution of fairness

arXiv

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arXiv 2017年

作者： Zhang, Yanling Fu, Feng Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China Department of Mathematics Dartmouth College HanoverNH United States Department of Biomedical Data Science Geisel School of Medicine Dartmouth College HanoverNH United States

Masses of experiments have shown individual preference for fairness which seems irrational. The reason behind it remains a focus for research. The effect of spite (individuals are only concerned with their own relative standing) on the evolution of fairness has attracted increasing attention from experiments, but only has been implicitly studied in one evolutionary model. The model did not involve high-offer rejections, which have been found in the form of non-monotonic rejections (rejecting offers that are too high or too low) in experiments. Here, we introduce a high offer and a non-monotonic rejection in structured populations of finite size, and use strategy intervention to explicitly study how spite influences the evolution of fairness: five strategies are in sequence added into the competition of a fair strategy and a selfish strategy. We find that spite promotes fairness, altruism inhibits fairness, and the non-monotonic rejection can cause fairness to overcome selfishness, which cannot happen without high-offer rejections. Particularly for the group-structured population with seven discrete strategies, we analytically study the effect of population size, mutation, and migration on fairness, selfishness, altruism, and spite. A larger population size cannot change the dominance of fairness, but it promotes altruism and inhibits selfishness and spite. Intermediate mutation maximizes selfishness and fairness, and minimizes spite;intermediate mutation maximizes altruism for intermediate migration and minimizes altruism otherwise. The existence of migration inhibits selfishness and fairness, and promotes altruism;sufficient migration promotes spite. Our study may provide important insights into the evolutionary origin of fairness. Copyright © 2017, The Authors. All rights reserved.

关键词： Population statistics

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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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BeCFI: Detecting hidden control flow with performance monitoring counters

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International Journal of High Performance Computing and Networking 2016年第5-6期9卷 470-479页

作者： Zhou, Hong Wei Shi, Wen Chang Yuan, Jin Hui Li, Fu Lin Institute of Electronic Technology Information Engineering University Zhengzhou China Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education Renmin University of China Beijing China

Most of existing control flow integrity efforts target keeping intended control flow in good integrity. However, they fail to expose hidden control flow that may be introduced by the execution of rootkits, ROP gadgets, etc. To overcome the challenge, we propose an innovative approach BeCFI to detect hidden control flow based on cross-view principle. Since modern processors are capable of observing the execution of all branch instructions, BeCFI obtains the hardware view with the support of performance monitoring counters (PMCs). To obtain software view, we build a software-based counter by compiler-patching and binary-overwriting, and monitor the execution of branch instructions with software-based counters. If a control transfer only appears in hardware view, BeCFI considers that it is hidden control transfer. We have developed a prototype system on Intel x86 Linux kernel. Our evaluations show BeCFI is capable of detecting the hidden control flow introduced by kernel rootkits and ROP attacks. Furthermore our performance tests demonstrate that BeCFI incurs an acceptable overhead. Copyright © 2016 Inderscience Enterprises Ltd.

关键词： Malware

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Differentially private density estimation via Gaussian mixtures model

Differentially private density estimation via Gaussian mixtu...

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International Workshop on Quality of Service

作者： Yuncheng Wu Yao Wu Hui Peng Juru Zeng Hong Chen Cuiping Li Renmin University of China Beijing P.R. China Ministry of Education Key Laboratory of Data Engineering and Knowledge Engineering

ISBN: (纸本)9781509026357

Density estimation can construct an estimate of the probability density function from the observed data. However, such a function may compromise the privacy of individuals. A notable paradigm for offering strong privacy guarantees in data analysis is differential privacy. In this paper, we propose DPGMM, a parametric density estimation algorithm using Gaussian mixtures model (GMM) under differential privacy. GMM is a well-known model that could approximate any distribution and can be solved via Expectation-Maximization (EM) algorithm. The main idea of DPGMM is to add two extra steps after getting the estimated parameters in the M step of each iteration. The first step is the noise adding step, which injects calibrated noise to the estimated parameters according to their L 1 -sensitivities and privacy budgets. The second step is the post-processing step, which post-processes those noisy parameters that might break their intrinsic characteristics. Extensive experiments using both real and synthetic datasets evaluate the performance of DPGMM, and demonstrate that the proposed method outperforms a state-of-art approach.

关键词： Privacy Gaussian distribution Estimation data privacy Covariance matrices Noise measurement data analysis

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Reverse Nearest Neighbor Heat Maps: A Tool for Influence Exploration

Reverse Nearest Neighbor Heat Maps: A Tool for Influence Exp...

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IEEE International Conference on data engineering

作者： Yu Sun Rui Zhang Andy Yuan Xue Jianzhong Qi Xiaoyong Du Department of Computing and Information Systems University of Melbourne Renmin University of China and Key Laboratory of Data Engineering and Knowledge Engineering MOE

ISBN: (纸本)9781509020218

We study the problem of constructing a reverse nearest neighbor (RNN) heat map by finding the RNN set of every point in a two-dimensional space. Based on the RNN set of a point, we obtain a quantitative influence (i.e., heat) for the point. The heat map provides a global view on the influence distribution in the space, and hence supports exploratory analyses in many applications such as marketing and resource management. To construct such a heat map, we first reduce it to a problem called Region Coloring (RC), which divides the space into disjoint regions within which all the points have the same RNN set. We then propose a novel algorithm named CREST that efficiently solves the RC problem by labeling each region with the heat value of its containing points. In CREST, we propose innovative techniques to avoid processing expensive RNN queries and greatly reduce the number of region labeling operations. We perform detailed analyses on the complexity of CREST and lower bounds of the RC problem, and prove that CREST is asymptotically optimal in the worst case. Extensive experiments with both real and synthetic data sets demonstrate that CREST outperforms alternative algorithms by several orders of magnitude.

关键词： Heating Radio frequency Indexes

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Search Result Diversification Based on Query Facets

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Journal of Computer Science & Technology 2015年第4期30卷 888-901页

作者：胡莎窦志成王晓捷文继荣 School of Information Renmin University of China Beijing 100872 China Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education Beijing 100872 China

In search engines, different users may search for different information by issuing the same query. To satisfy more users with limited search results, search result diversification re-ranks the results to cover as many user intents as possible. Most existing intent-aware diversification algorithms recognize user intents as subtopics, each of which is usually a word, a phrase, or a piece of description. In this paper, we leverage query facets to understand user intents in diversification, where each facet contains a group of words or phrases that explain an underlying intent of a query. We generate subtopics based on query facets and propose faceted diversification approaches. Experimental results on the public TREC 2009 dataset show that our faceted approaches outperform state-of-the-art diversification models.

关键词： query intent query facet search result diversification

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Laplacian SVM for Learning from Label Proportions

Laplacian SVM for Learning from Label Proportions

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IEEE International Conference on data Mining Workshops (ICDM Workshops)

作者： Limeng Cui Zhensong Chen Fan Meng Yong Shi School of Computer and Control Engineering UCAS School of Economics and Management UCAS Key Laboratory on Big Data Mining and Knowledge Management CAS

ISBN: (纸本)9781509059119

Proportion-SVM has been deeply studied due to its broad application prospects, such as modeling voting behaviors and spam filtering. However, the geometric information has been widely ignored. Thus, current methods usually show sensitivity to noises. To address these problems, in this paper, we combine the proportion learning framework with Laplacian term. We exploit the advantages of Laplacian term. Extensive experiments show the effectiveness of our method.

关键词： Support vector machines Laplace equations Kernel Training Optimization data models data mining

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Formalizing UML Model Metrics Using Z Language

Formalizing UML Model Metrics Using Z Language

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作者： Fangjun Wu School of Information Technology Jiangxi University of Finance and Economics Jiangxi Key Laboratory of Data and Knowledge Engineering Jiangxi University of Finance and Economics

Till now, a large variety of researchers have carried out lots of efforts on object-oriented and UML model metrics from different views. They put forward numerous of metrics and carried out some series of theoretical and experimental verifications on understandability, analyzability, maintainability, fault-proneness, change-proneness and reuse. However, there is no formal semantic specification for UML model metrics, which may lead to potential semantic inconsistency and ambiguity. To solve this problem, this paper provided formalization for UML model metrics at the level of UML Meta models. This formalization can not only help people to understand the meaning of UML model metrics, but also can be used in the application domain of UML model metrics in a more rigorous way.

关键词： software measurement object-oriented UML class diagrams Z language empirical validation theoretical verification

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Broadband, Low-Crosstalk and Power-Efficient 32×32 Optical Switch on a Dual-Layer Si3N4-on-SOI Platform

Broadband, Low-Crosstalk and Power-Efficient 32×32 Optical ...

引用

Optical Fiber Communication Conference, OFC 2016

作者： Gao, Wei Li, Xin Lu, Liangjun Chen, Jianping Zhou, Linjie State Key Laboratory of Advanced Optical Communication Systems and Networks Shanghai Key Lab of Navigation and Location Services Shanghai Institute for Advanced Communication and Data Science Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China SJTU-Pinghu Institute of Intelligent Optoelectronics Pinghu314200 China

We demonstrate a 32×32 optical switch on a dual-layer Si3N4-on-SOI platform with low fiber-to-fiber insertion loss (9.61~14.51 dB), low crosstalk (~-35 dB), broad 3-dB bandwidth (~57 nm) and low power consumption...

ISBN: (纸本)9781943580071

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