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作者： Dong, Jichang Dai, Wei Liu, Ying Yu, Lean Wang, Jie School of Economics and Management University of Chinese Academy of Sciences Beijing100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China School of Economics and Management Beijing University of Chemical Technology Beijing100029 China Department of Civil and Environmental Engineering Stanford University StanfordCA94305 United States

In this study, to address search index selection and volatility problems, we propose a learning-based search index collection method that collects the search data fraction for modeling by learning the best criteria from robust statistics. Based on the fraction of collected search index from internet search engine (***) data sources, a novel model is formulated for Chinese stock market price forecasting. We empirically test our method on the two main Chinese stock market price indexes and discover that the prediction accuracy is equivalent or superior to the benchmarks from previous studies that used alternative search index collection methods or lagged data prediction models. All prediction results outstand the importance of an effective data collection method for the robustness of forecast models and demonstrate the utility of a learning-based collection method for addressing search index collection problem, leading to a significant improvement in Chinese stock market price prediction accuracy. © 2019 World Scientific Publishing Company.

关键词： Search engines

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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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High Performance Geological Disaster Recognition using Deep Learning

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Procedia Computer Science 2018年 139卷 529-536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 on optical image is one of the key techniques in disaster control and disaster relief. Comparing with optical images, remote sensing images contain much higher resolution and more visualized contents. In this paper, we propose a landslide recognition framework which trains a deep auto-encoder network on the compressed domain. ANN or SVM is used as the classifier for decision making. In addition, in order to meet the requirement of some real-time applications, a high performance training network on CUDA-enabled GPUs is designed and implemented. Experiments are conducted on optical images from Google Earth.

关键词： deep learning geological disaster recognition parallel computing CUDA

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Few-Shot Charge Prediction with Multi-grained Features and Mutual Information 20

Few-Shot Charge Prediction with Multi-grained Features and M...

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20th China National Conference on Computational Linguistics, CCL 2021

作者： Zhang, Han Dou, Zhicheng Zhu, Yutao Wen, Jirong School of Information Renmin University of China Beijing China Gaoling School of Artificial Intelligence Renmin University of China Beijing China Université de Montréal Montreal Canada Beijing Key Laboratory of Big Data Management and Analysis Methods Beijing China Key Laboratory of Data Engineering and Knowledge Engineering MOE Beijing China

ISBN: (纸本)9783030841850

Charge prediction aims to predict the final charge for a case according to its fact description and plays an important role in legal assistance systems. With deep learning based methods, prediction on high-frequency charges has achieved promising results but that on few-shot charges is still challenging. In this work, we propose a framework with multi-grained features and mutual information for few-shot charge prediction. Specifically, we extract coarse- and fine-grained features to enhance the model’s capability on representation, based on which the few-shot charges can be better distinguished. Furthermore, we propose a loss function based on mutual information. This loss function leverages the prior distribution of the charges to tune their weights, so the few-shot charges can contribute more on model optimization. Experimental results on several datasets demonstrate the effectiveness and robustness of our method. Besides, our method can work well on tiny datasets and has better efficiency in the training, which provides better applicability in real scenarios. © 2021, Springer Nature Switzerland AG.

关键词： Forecasting

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A hybrid model for stock price prediction based on multi-view heterogeneous data

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Financial Innovation 2024年第1期10卷 2411-2460页

作者： Wen Long Jing Gao Kehan Bai Zhichen Lu School of Economics and Management University of Chinese Academy of SciencesBeijing100190People’s Republic of China Research Center on Fictitious Economy and Data Science Chinese Academy of SciencesBeijing100190People’s Republic of China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of SciencesBeijing100190People’s Republic of China Department of Mathematics Beijing Jiaotong UniversityBeijing100044People’s Republic of China

Literature shows that both market data and financial media impact stock prices;however,using only one kind of data may lead to information ***,this study uses market data and news to investigate their joint impact on stock price ***,combining these two types of information is difficult because of their completely different *** study develops a hybrid model called MVL-SVM for stock price trend prediction by integrating multi-view learning with a support vector machine(SVM).It works by simply inputting heterogeneous multi-view data simultaneously,which may reduce information *** with the ARIMA and classic SVM models based on single-and multi-view data,our hybrid model shows statistically significant *** the robustness test,our model outperforms the others by at least 10%accuracy when the sliding windows of news and market data are set to 1–5 days,which confirms our model’s ***,trading strategies based on single stock and investment portfolios are constructed separately,and the simulations show that MVL-SVM has better profitability and risk control performance than the benchmarks.

关键词： Market data Financial news Support vector machine Multi-view learning Heterogeneous data

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A Study on The Multi-Attribute Decision Theory and Methods 9th

A Study on The Multi-Attribute Decision Theory and Methods

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9th International Conference on Information Technology and Quantitative management, ITQM 2022

作者： Feng, Jinyuan Yan, Yunxi Huang, Manling Du, Yang Lu, Zhichen Li, Biao School of Business Administration Faculty of Business Administration Southwestern University of Finance and Economics Chengdu611130 China College of Dance Minzu University of China Beijing China School of Economics and Management University of Chinese Academy of Sciences Beijing101408 China Research Center on Fictitious Economy and Data Science Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China

Multi-attribute decision-making problem (MADM) is a kind of multi-criteria decision-making (MCDM), which is often used to solve decision-making problems where the decision variables are discrete and the number of decision-making schemes is limited. This paper reviews the basic definition of multi-attribute decision making (MADM) through the performance evaluation of science and technology funds in a city, and then deduces the TODIM decision making method and TOPSIS decision making method in detail. Finally, the clue case is decided by combining these two decision making methods. This paper specifically shows the usage scenarios and limitations of the TODIM decision method and the TOPSIS decision method through the clue case, analyzes the advantages and disadvantages of the two methods through the comparison of the two methods, and puts forward suggestions for the clue case based on the results of the two schemes. © 2022 The Authors. Published by Elsevier B.V.

关键词： Decision theory

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The Further Development of Intelligent knowledge for Wisdom 9th

The Further Development of Intelligent Knowledge for Wisdom

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9th International Conference on Information Technology and Quantitative management, ITQM 2022

作者： Gu, Jifa Xiao, Xiao Zhang, Lingling Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China School of Economics and Management University of 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

This paper introduces some modes from D, I, K to W, M. From different angles we investigate the various process. Especially we utilize the intelligent knowledge to develop the whole process. Finally we design a new medical hall for workshop of meta-synthetic engineering for helping patient to get the appropriate treatment and useful knowledge for health. © 2022 The Authors. Published by Elsevier B.V.

关键词： Patient treatment

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Analysis of the Effectiveness of Carbon Emission Trading Market in China 10

Analysis of the Effectiveness of Carbon Emission Trading Mar...

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10th International Conference on Information Technology and Quantitative management, ITQM 2023

作者： Yu, Anqi Peng, Shuyang Li, Yizhi Guo, Kun 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 Dornslife School of Arts and Science University of Southern California Los AngelesCA90007 United States Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China

As global climate change intensifies, carbon emissions trading has become an important tool for countries to address climate change. China, as the world's largest carbon emitter, has been conducting carbon trading pilots since 2011, and officially launched a national carbon market in 2017. The construction of China's carbon market is crucial for China and global efforts to address climate change. Market efficiency is a key indicator of its success. This article aims to measure and analyze the effectiveness of China's carbon market, and to explore policy measures to enhance its effectiveness. It selected data from effective trading days and conducted an exploratory study on the effectiveness of China's pilot carbon markets. To analyze the effectiveness of the carbon market, we combined the efficient market hypothesis theory with the fractal market hypothesis theory, adopted the variance ratio test method, and the rescaled range analysis method to conduct a comprehensive analysis of the Chinese carbon market, while also accounting for the reasons behind their computational inconsistency. As our research shows that China's pilot carbon market has not reached the weak efficiency level, we have provided some suggestions based on the current operational status of China's carbon market. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the Tenth International Conference on Information Technology and Quantitative management.

关键词： Carbon

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Research on the quality evaluation strategy of multi-source heterogeneous aggregation data 3

Research on the quality evaluation strategy of multi-source ...

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2020 3rd International Symposium on big data and Applied Statistics, ISBDAS 2020

作者： Zhan, Gaofeng Jiang, Yong Wang, Xiaofeng Zhao, Yuyang Zhao, Tong First Research Institute of the Ministry of Public Security Beijing100048 China Beijing Langrun Yihua Technology Co. Ltd Beijing100429 China University of Chinese Academy of Sciences Beijing100049 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100049 China

In the era of big data, the application of multi-source heterogeneous aggregation data is more and more extensive. If the quality of aggregation data is uneven, it will bring a lot of troubles to the subsequent data mining, and then lead to inaccurate decision-making. A comprehensive quality evaluation method for aggregation data is proposed in this paper, based on factor analysis and multivariate variance analysis which is from the perspective of multivariate statistical inference. The case study shows that the method proposed in this paper is feasible and adaptive for the long-term evaluation of the quality of multi-source heterogeneous aggregation data. © Published under licence by IOP Publishing Ltd.

关键词： Quality control

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A novel fingerprint classification method based on deep learning

A novel fingerprint classification method based on deep lear...

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International Conference on Pattern Recognition

作者： Ruxin Wang Congying Han Tiande Guo School of Mathematical Science University of Chinese Academy of Sciences (UCAS) Beijing School of Mathematical Science Key Laboratory of Big Data Mining and Knowledge Management UCAS

ISBN: (纸本)9781509048489

Fingerprint classification is an effective technique for reducing the candidate numbers of fingerprints in the stage of matching in automatic fingerprint identification system (AFIS). In recent years, deep learning is an emerging technology which has achieved great success in many fields, such as image processing, computer vision. In this paper, we have a preliminary attempt on the traditional fingerprint classification problem based on the new depth neural network method. For the four-class problem, only choosing orientation field as the classification feature, we achieve 91.4% accuracy using the stacked sparse autoencoders (SAE) with three hidden layers in the NIST-DB4 database. And then two classification probabilities are used for fuzzy classification which can effectively enhance the accuracy of classification. By only adjusting the probability threshold, we get the accuracy of classification is 96.1% (setting threshold is 0.85), 97.2% (setting threshold is 0.90) and 98.0% (setting threshold is 0.95) with a single layer SAE. Applying the fuzzy method, we obtain higher accuracy.

关键词： Machine learning databases Feature extraction Training Fingerprint recognition Neural networks Image reconstruction

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