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Build a Tourism-Specific Sentiment Lexicon Via Word2vec

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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A novel self-adaptive density-based clustering algorithm for radar signal sorting

A novel self-adaptive density-based clustering algorithm for...

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IET International Radar Conference 2015

作者： Liu, Ying Cui, Hongyuan School of Computer and Control University of Chinese Academy of Sciences China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9781849199940

Radar signal sorting, which de-interleaves pulse sequences from different radar emitters, is one of the key techniques in Electronic Support Measures (ESM) system of modern warfare. As the electromagnetic environment in modern electronic wars is complex and dense, conventional multi-parameter de-interleaving techniques cannot achieve satisfactory accuracy. Although clustering algorithms have shown promising results in signal sorting, they all have their own weaknesses. In this paper, we propose a novel densitybased clustering algorithm targeting radar signal sorting problem. Our clustering algorithm determines the number of clusters and the density thresholds by the distribution of the data itself rather than depending on user experience as in K-Means or other existing density-based algorithms. Multiple density thresholds may be generated when necessary. We ran our algorithm on a set of simulation radar data. It outperforms DBSCAN-based signal sorting algorithm a lot in terms of accuracy, missing sorting rate and the error sorting in each set of parameters. In addition, it is observed that our algorithm is insensitive to the parameters, ratio factor, uniformity, and quantile. Thus, our proposed self-adaptive density-based clustering algorithm is promising in solving radar signal sorting problem in real electromagnetic environment.

关键词： Sorting

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Two-Stage Polishing Network for Camouflaged Object Detection 11th

Two-Stage Polishing Network for Camouflaged Object Detectio...

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11th International Conference on Image and Graphics, ICIG 2021

作者： Jiang, Xuan Wu, Zhe Zhang, Yajie Su, Li Huang, Qingming University of Chinese Academy of Sciences Beijing China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China Peng Cheng Laboratory Shenzhen China

ISBN: (纸本)9783030873547

Due to the needs of medical and military fields, Camouflaged Object Detection (COD) becomes one of important branches of object detection. It has gradually gained people’s attention in recent years. How to correctly locate camouflaged objects accurately segment them are the main problems in this field. The COD task is far different from SOD because of the more complex background with similar colors and textures. Therefore it is more challenging. At present, there are still few existing methods, and lacks targeted method for the edge detection problem. In this paper, we propose a novel Two-Stage Polishing Network (TSPNet). This network consists of Front Feature Fusion Module (FFFM) and Polishing Module (PM). FFFM adopts Cross-modal Feature Aggregation and Global and Local Feature Aggregation to capture global context information and detail local information simultaneously. Meanwhile, PM uses edge truth map as supervision imformation to further study the object edge features. The experiments was conducted on three available datasets and the result shows that the proposed framework outperforms state-of-the-arts. Besides, TSPNet is compact with 50% model size saving than existed COD models. © 2021, Springer Nature Switzerland AG.

关键词： Object detection

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Pyramid scheme in stock market:A kind of financial market simulation

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Chinese Physics B 2021年第9期30卷 624-632页

作者： Yong Shi Bo Li Guang-Le Du School of Economics and Management University of Chinese Academy of SciencesBeijing 100190China Research Center on Fictitious Economy and Data Science Chinese Academy of SciencesBeijing 100190China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of SciencesBeijing 100190China Wenzhou Institute University of Chinese Academy of SciencesWenzhou 325001China School of Physical Sciences University of Chinese Academy of SciencesBeijing 100049China

Artificial stock market simulation based on agent is an important means to study financial *** on the assumption that the investors are composed of a main fund,small trend and contrarian investors characterized by four parameters,we simulate and research a kind of financial phenomenon with the characteristics of pyramid *** simulation results and theoretical analysis reveal the relationships between the rate of return of the main fund and the proportion of the trend investors in all small investors,the small investors'parameters of taking profit and stopping loss,the order size of the main fund and the strategies adopted by the main *** work is helpful to explain the financial phenomenon with the characteristics of pyramid schemes in financial markets,design trading rules for regulators and develop trading strategies for investors.

关键词： pyramid scheme agent-based model artificial stock market herd behavior

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Analyzing Multiple Phenotypes Based on Principal Component Analysis

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Acta Mathematicae Applicatae Sinica 2022年第4期38卷 843-860页

作者： De-liang BU San-guo ZHANG Na LI School of Mathematical Sciences University of Chinese Academy of SciencesBeijing 100049China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of SciencesBeijing 100049China LSC NCMISAcademy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190China

Joint analysis of multiple phenotypes can have better interpretation of complex diseases and increase statistical power to detect more significant single nucleotide polymorphisms(SNPs)compare to traditional single phenotype analysis in genome-wide association *** component analysis(PCA),as a popular dimension reduction method,has been broadly used in the analysis of multiple *** PCA transforms the original phenotypes into principal components(PCs),it is natural to think that by analyzing these PCs,we can combine information across *** PCA-based methods can be divided into two categories,either selecting one particular PC manually or combining information from all *** this paper,we propose an adaptive principle component test(APCT)which selects and combines the PCs adaptively by using Cauchy combination *** proposed method can be seen as a generalization of traditional PCA based method since it contains two existing methods as special *** simulation shows that our method is robust and can generate powerful result in various *** real data analysis of stock mice data also demonstrate that our proposed APCT can identify significant SNPs that are missed by traditional methods.

关键词： multiple phenotypes principal component analysis cauchy combination method

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Grain Science and Technology Policies and Food Security in China:An Empirical Study Based on a Provincial Dynamic Panel Model

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Journal of Systems Science and Information 2020年第6期11卷 504-523页

作者： Changhong NIE Mingming CUI Xiuting LI Institute of Zoology Chinese Academy of SciencesBeijing 100101China School of Economics and Management University of Chinese Academy of SciencesBeijing 100190China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of SciencesBeijing 100190China

China’s grain science and technology policies have played an important role in the development of China’s food *** paper aims to examine the effects of China’s grain science and technology policies on food *** quantitatively assesses China’s food security by analyzing the main contents and development trends of China’s food science technology policies through the text metrology method,and then investigates the effects of grain science and technology policies on food security by employing a provincial dynamic panel *** results show that food security in China is all-round developed,and that the release frequency and cumulative effect of grain science and technology policies play a significant role in promoting food *** grain science and technology policies can effectively guarantee China’s food security.

关键词： grain science and technology policy food security dynamic panel regression policy effect

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research on Pricing of data Based on Bi-level Programming Model

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Annals of data Science 2024年第4期11卷 1391-1419页

作者： Ding, Yurong Tian, Yingjie School of Economics and Management University of Chinese Academy of Sciences Beijing100190 China 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 MOE Social Science Laboratory of Digital Economic Forecasts and Policy Simulation at UCAS Beijing100190 China

Effective value measurement and pricing methods can greatly promote the healthy development of data sharing, exchange and reuse. However, the uncertainty of data value and neglect of interactivity lead to information asymmetry in the transaction process. A perfect pricing system and well-designed data trading market (hereafter called data market) can widely promote data transactions. We take the three-agents data market as an example to construct a sound data trading process. The data owner who provides data records, the model buyer who is interested in buying machine learning (ML) model instances, and the data broker who interacts between the data owner and the model buyer. Based on the characteristics of data market, like truthfulness, revenue maximization, version control, fairness and non-arbitrage, we propose a data pricing methods based on different model versions. Firstly, we utilize market research and construct a revenue maximization (RM) problem to price the different versions of ML models and solve it with the RM-ILP process. However, the RM model based on market research has two major problems: one is that the model buyer has no incentive to tell the truth, that is, the model buyer will lie in the market research to obtain a lower model price;the other is that it asks the data broker to release version menu in advance, resulting in an inefficient operation of the data market. In view of the defects of the RM transaction model, we propose a model buyers behavior analysis, establish the revenue maximization function based on different data versions to establish a bi-level linear programming model. We further add the incentive compatibility constraint and the individual rationality constraint, taking the utility of the model buyer and the revenue of the data broker into account. This reflects the consumer driven model in the data transaction mode. Finally, the RM-BLP process is proposed to transform RM problem into an equivalent single-level integer program

关键词： Costs

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Cooperation in Two-Stage Games on Undirected Networks

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Journal of Systems Science & Complexity 2017年第3期30卷 680-693页

作者： GAO Hongwei PETROSYAN Leon QIAO Han SEDAKOV Artem School of Mathematics and Statistics Qingdao UniversityQingdao 266071China. Faculty of Applied Mathematics and Control Processes Saint Petersburg State UniversitySaint Petersburg 198504Russia School of Economics and Management University of Chinese Academy of SciencesBeijing 100190China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of SciencesBeijing 100190China

In the paper, cooperative two-stage network games are studied. At the first stage of the game, players form a network, while at the second stage players choose their behaviors according to the network realized at the first stage. As a cooperative solution concept in the game, the core is *** is proved that some imputations from the core are time inconsistent, whereas one can design for them a time-consistent imputation distribution procedure. Moreover, the strong time consistency problem is also investigated.

关键词： Characteristic function cooperation core imputation imputation distribution proce-dure network strong time consistency T-value.

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China’s NTB Market: A Liquidity Dilemma

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Journal of Systems Science & Complexity 2018年第2期31卷 493-507页

作者： LI Yumeng MENG Xiangying ZHANG Yueyan WEI Xianhua School of Economics and Management University of Chinese Academy of Sciences Beijing 100190 China Key Laborutory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing 100190 China Sino-Danish Center for Education and Research University of Chinese Academy of Sciences Beijing 100190 China The State Key Laboratory of Automotive Safety and Energy Tsinghua University Beijing 100084 China Department of Automotive Engineering Tsinghua University Beijing 100084 China

This paper introduces China＇s New Third Board Market（the NTB market） and tests the market-making system in the role of improving market liquidity. The NTB Market is the main part of China＇s OTC market, established in 2006. In comparing with China＇s Main Board Market and the Second Board Market, it attracts a lot of start-up companies that need financing with less strict listing requirements. Meanwhile, it is full of opportunities and challenges that appeal to numerous securities traders and investors with the rapid development momentum. Since its establishment, the NTB Market has been stuck in the lack of liquidity. The authority of the NTB Market introduced market maker system, so as to solve the problem of liquidity. Using the difference-in-difference method（DID method）, the market maker system has been proved to make little contribution to improving the stock liquidity. Although the problem of liquidity is difficult to solve in short term, the NTB Market is full of opportunities for all kinds of participants definitely.

关键词： China＇s stock market DID method liquidity market makers.

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Robustness and orthogonality: Time series forecasting via wavelets

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Information Sciences 2025年 717卷

作者： Yingjie Tian Siyu Zhao Xingyu Zhang School of Economics and Management University of Chinese Academy of Sciences Beijing 100190 China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing 100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing 100190 China School of Mathematical Sciences University of Chinese Academy of Sciences Beijing 100049 China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing 101408 China National Key Laboratory of Space Integrated Information System Institute of Software Chinese Academy of Sciences Beijing 100190 China

Time series forecasting (TSF) is an important issue that spans various fields in daily life. With advancements in deep learning, numerous TSF methods have been developed leveraging both time and frequency domain characteristics of time series data. However, existing TSF methods primarily concentrate on data characteristics, yet which key property truly enhances model performance has not been thoroughly explored. Especially, for frequency domain-based methods, the reliance on stringent assumptions like the Linear Time-Invariant (LTI) system limits their applicability to non-stationary time series data. To address these issues, this paper introduces a novel TSF method called Robust Orthogonal decomposition via Wavelets (ROW). ROW employs wavelet transform to remove reliance on LTI assumptions and can capture local characteristics. Furthermore, this paper provides robustness analysis of ROW, i.e., the impact of orthogonal wavelet basis functions on model robustness. Through two examples, we illustrate that the robustness constant of ROW has a tighter upper bound under orthogonal wavelet basis, which indicates that ROW is more robust under perturbations. Experimental results also validate the effectiveness and robustness of ROW compared to existing methods.

关键词： Wavelet decomposition

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