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A data-Driven Dynamic Programming Model for Research Position Demand Forecasting

Annals of data Science 2017年第1期4卷 19-30页

作者： Xie, Yongjia Wu, Dengsheng Chen, Yuanping Jiao, Wenbin Li, Jianping Institute of Policy and Management Chinese Academy of Sciences Beijing100190 China Computer Network Information Center Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China

It has been worthy of notice that the number of scientific researchers has experienced a rapid growth in China. Meanwhile, the strict restriction to the total number and the position structure of researchers has exerted great pressure on the Chinese researchers. The decision makers have noticed this dilemma and a quantitative predicting result for decision support is in need. This paper puts forward a data-driven dynamic programming model to estimate the research position demand gap based on the thought of dynamic programming. This model fully considers the real practice of human resource management in scientific management in China. In the empirical study, the personnel data from 2006 to 2014, which are abstracted from the Academia Resource Planning system of the Chinese Academy of Sciences, are applied to the empirical analysis to estimate the human resource demand gap in the 13th Five Year Plan. The results show that there is a big demand gap of the research position on the whole in the next five years. © 2017, Springer-Verlag Berlin Heidelberg.

关键词： Dynamic programming

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Improved incremental local outlier detection for data streams based on the landmark window model

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knowledge AND INFORMATION SYSTEMS 2021年第8期63卷 2129-2155页

作者： Li, Aihua Xu, Weijia Liu, Zhidong Shi, Yong Cent Univ Finance & Econ Sch Management Sci & Engn Beijing 102206 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

Most existing algorithms of anomaly detection are suitable for static data where all data are available during detection but are incapable of handling dynamic data streams. In this study, we proposed an improved iLOF (incremental local outlier factor) algorithm based on the landmark window model, which provides an efficient method for anomaly detection in data streams and outperforms conventional methods. What is more, data windows as updating units are introduced to reduce the false alarm rate, and multiple tests are taken here to identify candidate anomalies and real anomalies. The improved iLOF shows its obvious advantage with its false positive rate. Furthermore, the proposed algorithm instantly deletes data points of identified real anomalies. We analyzed the performance of the improved algorithm and the sensitivity of certain parameters via empirical experiments using synthetic and real data sets. The experimental results demonstrate that the proposed improved algorithm achieved better performance on the higher detection rate and the lower false alarm rate compared with the original iLOF algorithm and its improvements.

关键词： Incremental local outlier factor algorithm Landmark window model Anomaly detection data streams

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An effective intrusion detection framework based on MCLP/SVM optimized by time-varying chaos particle swarm optimization

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NEUROCOMPUTING 2016年 199卷 90-102页

作者： Bamakan, Seyed Mojtaba Hosseini Wang, Huadong Tian Yingjie Shi, Yong Univ Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 10090 Peoples R China Univ Chinese Acad Sci Sch Econ & Management Beijing 10090 Peoples R China Univ Nebraska Coll Informat Sci & Technol Omaha NE 68182 USA

Many organizations recognize the necessities of utilizing sophisticated tools and systems to protect their computer networks and reduce the risk of compromising their information. Although many machine learning-based data classification algorithm has been proposed in network intrusion detection problem, each of them has its own strengths and weaknesses. In this paper, we propose an effective intrusion detection framework by using a new adaptive, robust, precise optimization method, namely, time varying chaos particle swarm optimization (TVCPSO) to simultaneously do parameter setting and feature selection for multiple criteria linear programming (MCLP) and support vector machine (SVM). In the proposed methods, a weighted objective function is provided, which takes into account trade-off between the maximizing the detection rate and minimizing the false alarm rate, along with considering the number of features. Furthermore, to make the particle swarm optimization algorithm faster in searching the optimum and avoid the search being trapped in local optimum, chaotic concept is adopted in PSO and time varying inertia weight and time varying acceleration coefficient is introduced. The performance of proposed methods has been evaluated by conducting experiments with the NSL-KDD dataset, which is derived and modified from well-known KDD cup 99 data sets. The empirical results show that the proposed method performs better in terms of having a high detection rate and a low false alarm rate when compared with the obtained results using all features. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Intrusion detection Support vector machine Parameter setting Feature selection

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Negative Overnight Returns: China's Security Markets

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Procedia Computer Science 2015年 55卷 980-989页

作者： Qingyuan Liu Hongbo Guo Xianhua Wei Key Laboratory of Big Data Mining and Knowledge Management School of Management University of Chinese Academy of Sciences Beijing 100190 China

We find that there exist statistically significant negative overnight returns in China's security markets, which is totally different from the previous research on HS300 Index by He et al. (2013), and the negative overnight returns are comparatively larger in China's GEM (Growth Enterprise Market) board and SME (Small and Medium Enterprise) board than in the mainboards of Shanghai and Shenzhen security markets. We also find some of the SWS Primary Sectors have negative overnight returns after ticking out of market effects, which can be a great guide for investing in hedging portfolios of specific sectors.

关键词： Negative overnight returns HS300 Index Hedging portfolios SSE50 Index SWS Primary Sector

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Improved least squares support vector machine based on metric learning

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NEURAL COMPUTING & APPLICATIONS 2018年第7期30卷 2205-2215页

作者： Li, Dewei Tian, Yingjie Univ Chinese Acad Sci Sch Math Sci Beijing 100049 Peoples R China 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

As two kinds of popular data mining methods, metric learning and SVM have a interesting and valuable internal relationship. The basic idea of metric learning is to learn a data-dependent metric, instead of Euclidean metric, to shrink the distances between similar points and extend the distances between dissimilar points. From a different view, LSSVM can reach a similar goal as metric learning. It finds two parallel hyperplanes to make the distances between points and corresponding hyperplane as small as possible and the distance between two hyperplanes as large as possible. LSSVM can be looked as a slack version of metric learning. Then, it can be improved by modifying the way in measuring between-class distance, lead to the raise of our novel approach ML-LSSVM, which adds constraints of inter-class distance into LSSVM. Alternating direction method of multipliers algorithm was implemented to solve ML-LSSVM effectively, much faster than handling the original quadratic convex programming problem. Experiments were made to validate the efficacy of ML-LSSVM and prove that different measurements of intra-class distance and inter-class distance have significant impact on classification. At last, the relation between LMNN and ML-LSSVM was discussed to illustrate that the local formulation of LMNN is equivalent to ML-LSSVM.

关键词： Metric learning Least square-SVM LMNN Classification Distance

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Support vector machine classifier with truncated pinball loss

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PATTERN RECOGNITION 2017年 68卷 199-210页

作者： Shen, Xin Niu, Lingfeng Qi, Zhiquan Tian, Yingjie Univ Chinese Acad Sci Sch Math Sci Beijing 100049 Peoples R China 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

Feature noise, namely noise on inputs is a long-standing plague to support vector machine(SVM). Conventional SVM with the hinge loss(C-SVM) is sparse but sensitive to feature noise. Instead, the pinball loss SVM(pin-SVM) enjoys noise robustness but loses the sparsity completely. To bridge the gap between C-SVM and pin-SVM, we propose the truncated pinball loss SVM((pin) over bar -SVM) in this paper. It provides a flexible framework of trade-off between sparsity and feature noise insensitivity. Theoretical properties including Bayes rule, misclassification error bound, sparsity, and noise insensitivity are discussed in depth. To train (pin) over bar -SVM, the concave-convex procedure(CCCP) is used to handle non-convexity and the decomposition method is used to deal with the subproblem of each CCCP iteration. Accordingly, we modify the popular solver LIBSVM to conduct experiments and numerical results validate the properties of (pin) over bar -SVM on the synthetic and real-world data sets. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Pinball loss Feature noise Sparsity Support vector machine

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Parameterization of rational translational surfaces

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THEORETICAL COMPUTER SCIENCE 2020年 835卷 156-167页

作者： Perez-Diaz, Sonia Shen, Li-Yong Univ Alcala Dept Fis & Matemat E-28871 Madrid Spain 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

A rational translational surface is a typical modeling surface used in computer-aided design and the architecture industry. In this study, we determine whether a given algebraic surface implicitly defined as V is a rational translational surface or not. This problem is reduced to finding the rational parameterizations of two space curves. More important, our discussions are constructive, and thus if V is translational, we provide a parametric representation of V of the form P(t(1), t(2)) = P-1(t(1)) + P-2(t(2)). (C) 2017 Elsevier B.V. All rights reserved.

关键词： Rational parameterization Reparameterization Translational surface

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Math Word Problem Generation via Disentangled Memory Retrieval

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ACM TRANSACTIONS ON knowledge DISCOVERY FROM data 2024年第5期18卷 1-21页

作者： Qin, Wei Wang, Xiaowei Hu, Zhenzhen Wang, Lei Lan, Yunshi Hong, Richang Hefei Univ Technol Minist Educ Key Lab Knowledge Engn Big Data Tuxin Rd 193 Hefei 230009 Peoples R China Singapore Management Univ Sch Comp & Informat Syst Singapore Singapore East China Normal Univ Sch Data Sci & Engn Shanghai Peoples R China

The task of math word problem (MWP) generation, which generates an MWP given an equation and relevant topic words, has increasingly attracted researchers' attention. In this work, we introduce a simple memory retrieval module to search related training MWPs, which are used to augment the generation. To retrieve more relevant training data, we also propose a disentangled memory retrieval module based on the simple memory retrieval module. To this end, we first disentangle the training MWPs into logical description and scenario description and then record them in respective memory modules. Later, we use the given equation and topic words as queries to retrieve relevant logical descriptions and scenario descriptions from the corresponding memory modules, respectively. The retrieved results are then used to complement the process of the MWP generation. Extensive experiments and ablation studies verify the superior performance of our method and the effectiveness of each proposed module. The code is available at https://***/mwp-g/MWPG-DMR.

关键词： Memory retrieval math word problem text generation

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PSGAN: A Minimax Game for Personalized Search with Limited and Noisy Click data 19

PSGAN: A Minimax Game for Personalized Search with Limited a...

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42nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR)

作者： Lu, Shuqi Dou, Zhicheng Xu, Jun Nie, Jian-Yun Wen, Ji-Rong Renmin Univ China Sch Informat Beijing Peoples R China Univ Montreal DIRO Montreal PQ Canada Beijing Key Lab Big Data Management & Anal Method Beijing Peoples R China MOE Key Lab Data Engn & Knowledge Engn Beijing Peoples R China

ISBN: (纸本)9781450361729

Personalized search aims to adapt document ranking to user's personal interests. Traditionally, this is done by extracting click and topical features from historical data in order to construct a user profile. In recent years, deep learning has been successfully used in personalized search due to its ability of automatic feature learning. However, the small amount of noisy personal data poses challenges to deep learning models to learn the personalized classification boundary between relevant and irrelevant results. In this paper, we propose PSGAN, a Generative Adversarial Network (GAN) framework for personalized search. By means of adversarial training, we enforce the model to pay more attention to training data that are difficult to distinguish. We use the discriminator to evaluate personalized relevance of documents and use the generator to learn the distribution of relevant documents. Two alternative ways to construct the generator in the framework are tested: based on the current query or based on a set of generated queries. Experiments on data from a commercial search engine show that our models can yield significant improvements over state-of-the-art models.

关键词： personalized web search generative adversarial network

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COMPREHENSIVE ANALYSIS OF OVER-SMOOTHING IN GRAPH NEURAL NETWORKS FROM MARKOV CHAINS PERSPECTIVE

arXiv

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arXiv 2022年

作者： Zhao, Weichen Wang, Chenguang Han, Congying Guo, Tiande Key Laboratory of Big Data Mining and Knowledge Management CAS Beijing China

The over-smoothing problem is an obstacle of developing deep graph neural network (GNN). Although many approaches to improve the over-smoothing problem have been proposed, there is still a lack of comprehensive understanding and conclusion of this problem. In this work, we analyze the over-smoothing problem from the Markov chain perspective. We focus on message passing of GNN and first establish a connection between GNNs and Markov chains on the graph. GNNs are divided into two classes of operator-consistent and operator-inconsistent based on whether the corresponding Markov chains are time-homogeneous. Next we attribute the over-smoothing problem to the convergence of an arbitrary initial distribution to a stationary distribution. Based on this, we prove that although the previously proposed methods can alleviate over-smoothing, but these methods cannot avoid the over-smoothing problem. In addition, we give the conclusion of the over-smoothing problem in two types of GNNs in the Markovian sense. On the one hand, operator-consistent GNN cannot avoid over-smoothing at an exponential rate. On the other hand, operator-inconsistent GNN is not always over-smoothing. Further, we investigate the existence of the limiting distribution of the time-inhomogeneous Markov chain, from which we derive a sufficient condition for operator-inconsistent GNN to avoid over-smoothing. Finally, we design experiments to verify our findings. Results show that our proposed sufficient condition can effectively improve over-smoothing problem in operator-inconsistent GNN and enhance the performance of the model. © 2022, CC BY-NC-SA.

关键词： Message passing

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