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An agent-based model for investigating the impact of distorted supply-demand information on China's resale housing market

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JOURNAL OF COMPUTATIONAL SCIENCE 2018年 25卷 1-15页

作者： He, Zhou Dong, Jichang Yu, Lean Univ Chinese Acad Sci Sch Econ & Management Beijing 100190 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China Beijing Univ Chem Technol Sch Econ & Management Beijing 100029 Peoples R China

Trading participators suffer from information disadvantage in China's resale housing market, where brokers are able to distort supply-demand information and thus mislead their clients in price negotiation. In this paper, we propose an agent-based resale model to examine how brokers' distorted market information affects the market performances. Experimental results show that brokers are truly able to influence market trade count, resale price per size, and the benefits and costs of buyers and sellers, We find that no matter what the actual market condition is, assisting sellers is always the dominating policy for rational brokers. We also find that both buyers and sellers are likely to gain more surplus from transactions if they sign an "Exclusive Right To Buy/Sell" agreement with the broker, but the sellers are less bothered by the increased trade time. Coupled with these findings, managerial implications are discussed for China's resale housing market. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Agent-based modelling Resale housing market Distorted information Broker

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Intrinsic or Extrinsic Evaluation: An Overview of Word Embedding Evaluation 18

Intrinsic or Extrinsic Evaluation: An Overview of Word Embed...

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18th IEEE International Conference on data mining Workshops (ICDMW)

作者： Shi, Yong Zheng, Yuanchun Guo, Kun Zhu, Luyao Qu, Yi Univ Nebraska Coll Informat Sci & Technol Omaha NE 68182 USA Chinese Acad Sci Sch Econ & Management Key Lab Big Data Min & Knowledge Management Beijing Peoples R China Univ Chinese Acad Sci Sch Comp & Control Engn Beijing Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing Peoples R China

ISBN: (纸本)9781538692882

Compared with traditional methods, word embedding is an efficient language representation that can learn syntax and semantics by using neural networks. As the result, more and more promising experiments in natural language processing (NLP) get the state-of-the-art results by introducing word embedding. In principle, embedding representation learning embeds words to a low-dimensional vector space, therefore vectors support initialization of NLP tasks such as text classification, sentiment analysis, language understanding, etc. However, polysemy is very common in many languages, which causes word ambiguation, further influences the accuracy of the system. Additionally, language models based on distributed hypotheses mostly focused on word properties rather than morphology were our primary focus. This leads to unreasonable performance in different evaluations. At the same time, word embedding learning and measuring are two vital components of word representation. In this paper, we overviewed many language models including single sense and multiple sense word embedding, and many evaluated approaches including intrinsic and extrinsic evaluation. We found that there are obvious gaps between vectors and manual annotations in word similarity evaluation, and language models that achieved good performance in intrinsic evaluations could not produce similar results in extrinsic evaluations. To the best of our knowledge, there is no universal language model and embedding learning method for most NLP task, and each evaluations also hidden natural defects compared to human knowledge. More evaluated datasets are also investigated such as datasets used in intrinsic and extrinsic evaluations. We believe that an improved evaluation dataset and a more rational evaluation method would benefit from this overview.

关键词： word embedding language model intrinsic evaluation extrinsic evaluation

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Urban tourism competitiveness evaluation system and its application: Comparison and analysis of regression and classification methods

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Procedia Computer Science 2019年 162卷 429-437页

作者： Shuze Guo Yao Jiang Wen Long China University of Political Science and Law Fuxue Road #27Beijing 102220 China School of Economics & Management University Chinese Academy of Sciences Beijing 100190 China Key Laboratory of Big Data Mining & Knowledge Management Chinese Academy of Sciences Beijing 100190 P.R. China

Under the background of economic transformation, tourism is one of the most dynamic and promising tertiary industries. How to improve the competitiveness of tourism has become a new idea for industrial upgrading in various regions. This paper builds an evaluation system consisting of five major aspects, based on the data of 75 tourist destinations; then, uses cluster analysis to classify cities, and uses logistic regression, SVM and random forest methods to predict the tourism competitiveness of sample cities and compare the advantages and disadvantages of the two methods - classification and regression. From the results of the empirical test, the results of the classification method are generally better than the results of the regression, and in the classification method, the results of the SVM are better than the results of the random forest. In this case, the SVM model gives full play to its ability to solve the problem of nonlinear classification.

关键词： tourism competitiveness cluster analysis logistics regression SVM random forest

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Consistent test for parametric models with right-censored data using projections

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COMPUTATIONAL STATISTICS & data ANALYSIS 2018年 118卷 112-125页

作者： Sun, Zhihua Ye, Xue Sun, Liuquan Univ Chinese Acad Sci Beijing Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing Peoples R China Chinese Acad Sci Acad Math & Syst Sci Beijing Peoples R China

In the literature, there are several methods to test the adequacy of parametric models with right-censored data. However, these methods will lose effect when the predictors are medium-high dimensional. In this study, a projection-based test method is built, which acts as if the predictors were scalar even if they are multidimensional. The proposed test is shown to be consistent and can detect the alternative hypothesis converging to the null hypothesis at the rate n(-r) with 0 <= r <= 1/2. Also, it is free from the choices of the subjective parameters such as bandwidth, kernel and weighting function. A wild bootstrap method is developed to determine the critical value of the test, which is shown to be robust to the model conditional heteroskedasticity. Simulation studies and real data analyses are conducted to validate the finite sample behavior of the proposed method. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Consistent test Curse-of-dimensionality-free Empirical process Projection Right-censored data

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Build a Tourism-Specific Sentiment Lexicon Via Word2vec

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Annals of data Science 2018年第1期5卷 1-7页

作者： Li, Wei Zhu, Luyao Guo, Kun Shi, Yong Zheng, Yuanchun School of Economics and Management University of Chinese Academy of Sciences Beijing China Fictitious Economy and Data Science Research Center Chinese Academy of Sciences Beijing China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China College of Information Science and Technology University of Nebraska at Omaha OmahaNE68182 United States

Online travel and online travel culture developed fast in China recently years while useful knowledge still hidden under a large number of tourism reviews. Therefore, we need effective sentiment analysis methods to mine useful knowledge which can help tourism websites make decisions and improve their travel products. Some data-driven sentiment lexicons have poor performance on sentiment polarity classification due to lack of semantic information. Thus, we propose an effective and more proper data-driven sentiment lexicon construction method incorporating manually labeled sentiment scores, semantic similarity information that is introduced by machine learning method word2vec. Experimental results demonstrate that our method improves the performance of tourism sentiment analysis significantly. © 2018, Springer-Verlag GmbH Germany.

关键词： Sentiment analysis

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DC decomposition based branch-and-bound algorithms for box-constrained quadratic programs

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OPTIMIZATION LETTERS 2018年第5期12卷 985-996页

作者： Lu, Cheng Deng, Zhibin North China Elect Power Univ Sch Econ & Management Beijing 102206 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 100190 Peoples R China

The difference-of-convex (DC) decomposition is an effective method for designing a branch-and-bound algorithm. In this paper, we design two new branch-and-bound algorithms based on DC decomposition, to find global solutions of nonconvex box-constrained quadratic programming problems, and compare the efficiency of the proposed algorithms with two previous state-of-the-art branch-and-bound algorithms. Numerical experiments are conducted to show the competitiveness of the proposed algorithms on 20-60 dimensional box-constrained quadratic programming problems.

关键词： Quadratic program Difference-of-convex Branch-and-bound algorithm Global optimization Convex relaxation

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A Brief Survey of Relation Extraction Based on Distant Supervision 19th

A Brief Survey of Relation Extraction Based on Distant Super...

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19th Annual International Conference on Computational Science (ICCS)

作者： Shi, Yong Xiao, Yang Niu, Lingfeng Univ Chinese Acad Sci Sch Comp & Control Engn Beijing 100190 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 100190 Peoples R China Chinese Acad Sci Res Ctr Fictitious Econ & Data Sci Beijing 100190 Peoples R China Univ Nebraska Omaha Coll Informat Sci & Technol Omaha NE 68182 USA

ISBN: (纸本)9783030227449;9783030227432

As a core task and important part of Information ExtractionEntity Relation Extraction can realize the identification of the semantic relation between entity pairs. And it plays an important role in semantic understanding of sentences and the construction of entity knowledge base. It has the potential of employing distant supervision method, end-to-end model and other deep learning model with the creation of large datasets. In this review, we compare the contributions and defect of the various models that have been used for the task, to help guide the path ahead.

关键词： Relation extraction Deep learning Distant supervision

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The dynamical relationship between capital market and macroeconomy: based on dynamic Bayesian network 7

The dynamical relationship between capital market and macroe...

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7th International Conference on Information Technology and Quantitative management (ITQM) - Information Technology and Quantitative management Based on Artificial Intelligence

作者： Liu, Yue Wang, Yijing Zheng, Guihuan Wang, Jue Guo, Kun Univ Chinese Acad Sci Sch Econ & Management Beijing 100190 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 China Secur Regulatory Commiss China Inst Finance & Capital Markets Beijing 100032 Peoples R China Chinese Acad Sci Acad Math & Syst Sci Beijing 100190 Peoples R China

The stock market volatility will be affected by many economic factors. On the contrary, the stock market will also influence the formulation of the economy and policy. It will be very useful if a qualitative structure can be computed to figure out the corresponding level between the stock market with these factors. The dynamical Bayesian factor graph is a suitable method to solve this problem. We choose a series of economic factors to represent the possible influence from macroeconomy, and macro-policy. We have found that the changes in the stock market during the bull market will affect the macroeconomy factors, and this effect will continue to weaken as the stock market moves to the bear market. (C) 2020 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-ne-nd/4.0/) Peer-review under responsibility of the scientific committee of the 7th International Conference on Information Technology and Quantitative management (ITQM 2019)

关键词： Dynamical Bayesian factor graph Macroeconomic factors Macro-policy factors Stock market

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Robust outcome weighted learning for optimal individualized treatment rules

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JOURNAL OF BIOPHARMACEUTICAL STATISTICS 2019年第4期29卷 606-624页

作者： Fu, Sheng He, Qinying Zhang, Sanguo Liu, Yufeng Natl Univ Singapore Dept Ind & Syst Engn Singapore Singapore South China Agr Univ Coll Econ & Management Guangzhou Guangdong 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 Univ N Carolina Dept Stat & Operat Res Dept GenetDept Biostat Carolina Ctr Genome SciLineberger Comprehens Can Chapel Hill NC 27599 USA

Personalized medicine has received increasing attentions among scientific communities in recent years. Because patients often have heterogenous responses to treatments, discovering individualized treatment rules (ITR) is an important component of precision medicine. To that end, one needs to develop a proper decision rule using patient-specific characteristics to maximize the expected clinical outcome, i.e. the optimal ITR. Recently, outcome weighted learning (OWL) has been proposed to estimate optimal ITR under a weighted classification framework. Since most of commonly used loss functions are unbounded, the resulting ITR may suffer similar effects of outliers as the corresponding classifiers. In this paper, we propose robust OWL (ROWL) to build more stable ITRs using a new family of bounded and non-convex loss functions. Moreover, we extend the proposed ROWL method to the multiple treatment setting under the angle-based classification structure. Our theoretical results show that ROWL is Fisher consistent, and can provide the estimation of rewards' ratios for the resulting ITRs. We develop an efficient difference of convex functions algorithm (DCA) to solve the corresponding nonconvex optimization problem. Through analysis of simulated examples and a real medical dataset, we demonstrate that the proposed ROWL method yields more competitive performance in terms of the empirical value function and the misclassification error than several existing methods.

关键词： Angle-based classifiers multiple treatments non-convex optimization precision medicine robustness soft and hard classification

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Poisoning attack against estimating from pairwise comparisons

arXiv

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arXiv 2021年

作者： Ma, Ke Xu, Qianqian Zeng, Jinshan Cao, Xiaochun Huang, Qingming The School of Computer Science and Technology University of Chinese Academy of Sciences Beijing100049 China The Artificial Intelligence Research Center Peng Cheng Laboratory Shenzhen518055 China The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China The School of Computer and Information Engineering Jiangxi Normal University Jiangxi Nanchang330022 China Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China The School of Cyber Security University of Chinese Academy of Sciences Beijing100049 China The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China The School of Computer Science and Technology University of Chinese Academy of Sciences Beijing100049 China The Key Laboratory of Big Data Mining and Knowledge Management The School of Economics and Management University of Chinese Academy of Sciences Beijing100049 China

As pairwise ranking becomes broadly employed for elections, sports competitions, recommendation, information retrieval and so on, attackers have strong motivation and incentives to manipulate or disrupt the ranking list. They could inject malicious comparisons into the training data to fool the target ranking algorithm. Such a technique is called "poisoning attack" in regression and classification tasks. In this paper, to the best of our knowledge, we initiate the first systematic investigation of data poisoning attack on the pairwise ranking algorithms, which can be generally formalized as the dynamic and static games between the ranker and the attacker, and can be modeled as certain kinds of integer programming problems mathematically. To break the computational hurdle of the underlying integer programming problems, we reformulate them into the distributionally robust optimization (DRO) problems, which are computational tractable. Based on such DRO formulations, we propose two efficient poisoning attack algorithms and establish the associated theoretical guarantees including the existence of Nash equilibrium and the generalization ability bounds. The effectiveness of the suggested poisoning attack strategies is demonstrated by a series of toy simulations and several real data experiments. These experimental results show that the proposed methods can significantly reduce the performance of the ranker in the sense that the correlation between the true ranking list and the aggregated results with toxic data can be decreased dramatically. © 2021, CC BY-NC-ND.

关键词： Toys

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