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Threshold and Deadline Effect in Crowdfunding

Procedia Computer Science 2019年 162卷 339-346页

作者： Yan Song Xin Tian Chunlin Luo Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing 100190China School of Economics and Management University of Chinese Academy of Sciences Beijing 100190China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing 100190China School of Information Technology Jiangxi University of Finance and Economics Nanchang Jiangxi 330013China

The deadline effect and threshold effect are common in product development and group buying. We consider these two effects also exist in crowdfunding, affecting the crowdfunding performance in the future. We conduct an empirical study using a dataset from *** to examine these two effects and their interactive relationship on the following crowdfunding performance. We find the threshold effect exists in crowdfunding and has a positive influence on the following contribution behaviour. The deadline effect also can stimulate the crowdfunding behaviour via limited fundraising time. Furthermore, we find the threshold effect and deadline effect are complements that increase the amount pledged.

关键词： crowdfunding performance threshold effect deadline effect interactive effect

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Geo-spatial ontology matching through compact evolutionary algorithm 2nd

Geo-spatial ontology matching through compact evolutionary a...

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2nd International Conference on Smart Vehicular Technology, Transportation, Communication and Applications, VTCA 2018

作者： Xue, Xingsi Liu, Jianhua College of Information Science and Engineering Fujian University of Technology FuzhouFujian350118 China Intelligent Information Processing Research Center Fujian University of Technology FuzhouFujian350118 China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology FuzhouFujian350118 China Fujian Key Lab for Automotive Electronics and Electric Drive Fujian University of Technology FuzhouFujian350118 China

ISBN: (纸本)9783030045845

Geo-spatial ontologies can provide a formal description of concepts, relationships, activities, features and rules in GIS domain. However, simply use them only allows to partially solve semantic conflicts, and does not completely solve heterogeneity issues that are caused by themselves. Geo-spatial ontology matching technique can find the correspondences between semantic identical entities, and solve the heterogeneous problem between two geo-spatial ontologies. Be inspired by the successful application of Evolutionary Algorithm (EA) in instance matching domain, in this paper, it is utilized to match the heterogeneous geo-spatial ontologies. To reduce the runtime and memory consumption required by EA, a compact version of it is presented, which does not work on the whole population but a probability representation on it. In addition, a geo-spatial similarity measure is presented to determine the identical geo-spatial entities, and an optimal model is constructed for geo-spatial ontology matching problem. The experimental results show that cEA-based geo-spatial ontology matching technique can efficiently determine the alignment. © Springer Nature Switzerland AG 2019.

关键词： Evolutionary algorithms

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Automated vision inspection of rail surface cracks: A double-layer data driven framework 48

Automated vision inspection of rail surface cracks: A double...

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48th International Conference on Computers and Industrial engineering, CIE 2018

作者： Zhuang, Li Wang, Long Zhang, Zijun School of Data Science City University of Hong Kong Hong Kong Beijing100083 China Beijing Key Laboratory of Knowledge Engineering for Materials Science Beijing100083 China

A double-layer data-driven framework for the automated vision inspection of the rail surface cracks is proposed in this paper. Based on images of rails, the proposed framework is capable to detect the location of cracks firstly and next automatically obtain the boundary of cracks via a feature-based linear iterative crack aggregation (FLICA). Extended Haar-like features are applied to develop significant features for identifying cracks in images. Built on extended Haar-like features, a cascading classifier ensemble integrating three single cascading classifiers with a major voting scheme is proposed to decide the presence of cracks in the image. Each single cascading classifier is composed of a sequence of stage classifiers trained by the LogitBoost algorithm. A scalable sliding window carrying the cascading classifier ensemble is applied to scan images of rail tracks, which is identified by the Otsu’s method, and detect cracks. After completing the crack registration in the first layer, the FLICA is developed to discover boundaries of cracks. The effectiveness of the proposed data-driven framework for identifying rail surface cracks is validated with the rail images provided by the China Railway Corporation and Hong Kong Mass Transit Railway (MRT). Six benchmarking methods, the Otsu’s method, mean shift, the visual detection system, the geometrical approach, fully convolutional networks and the U-net, are utilized to prove advantages of the proposed framework. Results of the validation and comparative analyses demonstrate that the proposed framework is most effective in the rail surface crack detection. © 2018, Curran Associates Inc. All rights reserved.

关键词： Crack detection

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Research on Intelligent Frequency Control of Permanent Magnet Synchronous Motor

Research on Intelligent Frequency Control of Permanent Magne...

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International Conference on Intelligent Computing, Automation and Systems (ICICAS)

作者： Kuo-Chi Chang Zhongxin Hu Kai-Chun Chu Fu-Hsiang Chang Hsiao-Chuan Wang Tsui-Lien Hsu Yuh-Chung Lin Yuwen Zhou Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou China College of Mechanical & Electrical Engineering National Taipei University of Technology Taipei Taiwan North Borneo University College Sabah Malaysia Fujian University of Technology Fuzhou China Shih-Hsin University Taipei Taiwan Institute of Environmental Engineering National Taiwan University Taipei Taiwan Institute of Construction Engineering and Management National Central University Taoyuan Taiwan

The development of PMSM is inseparable from the continuous improvement of the performance-to-price ratio of permanent magnet materials and the development of power electronic equipment. This is exactly the study of PMSM. Entered a new stage, PMSM plays an important role in military, industry and daily life. Dating vector control theory to the field of PMSM speed regulation is also a major breakthrough. Researchers combined the mathematical model of PMSM with the vector control model. It is based on this that the PMSM vector control double closed-loop model was established. Simulate in MATLAB software. There are current module, speed module and PMSM main module in the simulation diagram, and then analyze the simulation results.

关键词： Computational modeling Simulation Software Regulation Power electronics Mathematical model Machine vector control

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A new algorithm for classification based on multi-classifiers learning 5th

A new algorithm for classification based on multi-classifier...

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5th International Conference on Geo-Spatial knowledge and Intelligence, GSKI 2017

作者： Zheng, Yifeng Li, Guohe Zhang, Wenjie School of Computer Sciences Key Laboratory of Data Science and Intelligence Application Fujian Province University Minnan Normal University Zhangzhou China College of Geophysics and Information Engineering Beijing Key Lab of Data Mining for Petroleum Data University China University of Petroleum Beijing China

ISBN: (纸本)9789811308956

Quality and quantity are the two key factors to influence the accuracy of classification. In order to improve the classification accuracy, in this paper, we propose a new algorithm, called CMCM (Classification based on Multiple Classifier Models), which consists of two classification models. In Model1, we mainly focus on the improvement of quality, thus the best attribute value from both the items and their complements in the training set is selected as the first item of a classification rule. While in Model2, quantity is taken into consideration, so it constructs two candidate sets and uses the one-versus-many strategy to generate several rules at one time. The experiment results show that: (1) Model1 can extract sufficient high quality rules and achieve high classification accuracy. (2) Model2 can extract sufficient information and achieve high classification accuracy. (3) CMCM can achieve higher classification accuracy compare with traditional classification. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Classification (of information)

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data Analysis of Portfolio Optimization using Artificial Neural Network in China’s Stock Market*

Data Analysis of Portfolio Optimization using Artificial Neu...

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Big data Intelligence and Computing (dataCOM), International Conference on

作者： Chia-Hung Wang Chia-Jung Lee College of Information Science and Engineering Fujian University of Technology Fuzhou City China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fuzhou City China School of Big Data Management Soochow University Taipei City Taiwan

ISBN: (数字)9781728141176

ISBN: (纸本)9781728141183

This paper presents a neural network model for studying the portfolio optimization in China’s stock market. An investment scheme is proposed by using an artificial intelligence algorithm based on the trained neural network. In the empirical study, six key indicators of historical transaction data for stocks are used as the input characteristics of our neural network model, which are the opening price, the closing price, the trading volume, the highest and lowest price on the day, and Week time scale. Besides, we use Python technology to process the collected stock data and develop the learning process of the studied neural network for predicting the closing price for the next period. Based on the presented data analysis in the paper, the investors of the stock market in China could make a rational decision-making on the portfolio optimization.

关键词： Solid modeling data analysis Neural networks Prediction algorithms data models Stock markets Artificial intelligence

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An Optimization-Based Truth Discovery Method with Claim Relation

An Optimization-Based Truth Discovery Method with Claim Rela...

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IEEE International Conference on Big knowledge (ICBK)

作者： Jiazhu Xia Ying He Yuxin Jin Xianyu Bao Gongqing Wu Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Ministry of Education Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China Shenzhen Academy of Inspection and Quarantine Shenzhen China

With the advent of the era of big data, the information from multi-sources often conflicts due to that errors and fake information are inevitable. Therefore, how to obtain the most trustworthy or true information (i.e. truth) people need gradually becomes a troublesome problem. In order to meet this challenge, a novel hot technology named truth discovery that can infer the truth and estimate the reliability of the source without supervision has attracted more and more attention. However, most existing truth discovery methods only consider that the information is either same or different rather than the fine-grained relation between them, such as inclusion, support, mutual exclusion, etc. Actually, this situation frequently exists in real-world applications. To tackle the aforementioned issue, we propose a novel truth discovery method named OTDCR in this paper, which can handle the fine-grained relation between the information and infer the truth more effectively through modeling the relation. In addition, a novel method of processing abnormal values is applied to the preprocessing of truth discovery, which is specially designed for categorical data with the relation. Experiments in real dataset show our method is more effective than several outstanding methods.

关键词： Task analysis knowledge engineering Big data Crowdsourcing Reliability engineering Meteorology

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Ensemble Classification Method Based on Truth Discovery

Ensemble Classification Method Based on Truth Discovery

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IEEE International Conference on Big knowledge (ICBK)

作者： Yuxin Jin Ze Yang Ying He Xianyu Bao Gongqing Wu Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Ministry of Education Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China Shenzhen Academy of Inspection and Quarantine Shenzhen China

Classification is a hot topic in such fields as machine learning and data mining. The traditional approach of machine learning is to find a classifier closest to the real classification function, while ensemble classification is to integrate the results of base classifiers, then make an overall prediction. Compared to using a single classifier, ensemble classification can significantly improve the generalization of the learning system in most cases. However, the existing ensemble classification methods rarely consider the weight of the classifier, and there are few methods to consider updating the weights dynamically. In this paper, we are inspired by the idea of truth discovery and propose a new ensemble classification method based on the truth discovery (named ECTD). As far as we know, we are the first to apply the idea of truth discovery in the field of ensemble learning. Experimental results demonstrate that the proposed method performs well in ensemble classification.

关键词： Reliability Machine learning Classification algorithms Task analysis Buildings Computational modeling knowledge engineering

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Consensus with Voting Theory in Blockchain Environments

Consensus with Voting Theory in Blockchain Environments

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IEEE International Conference on Big knowledge (ICBK)

作者： Lei Li Yongkang Jiang Guanfeng Liu Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology) Ministry of Education Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China Computing Department Macquarie University Sydney Australia

A blockchain can be taken as a decentralized and distributed public database. In order to achieve data consistency of the system nodes, the execution of a consensus algorithm is necessary and required in the case of decentralized environments. Simply speaking, the consensus is that every node agrees on some record in the blockchain. There are many kinds of consensus algorithms in blockchain environments, and each consensus algorithm has its own proper application scenario. Here we firstly analysis and compare various popular consensus algorithms in blockchain environments, and then as voting theory has systematically studied the decision-making in a group, the traditional methods of voting theory is summarized and listed, including (Position) scoring rules, Copeland, Maximin, Ranked pairs, Voting trees, Bucklin, Plurality with runoff, Single transferable vote, Baldwin rule, and Nanson rule. Finally, we introduce the voting methods from voting theory to consensus algorithms in the blockchain to improve its performance.

关键词： Blockchain Consensus algorithm Bitcoin Voting knowledge engineering Computer science Decision making

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Analysis of Vegetation Variation Around Zijinshan Gold and Copper Mine, Fujian, China

Analysis of Vegetation Variation Around Zijinshan Gold and C...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Mengjing Lin Xiaoqin Wang Aifang Xiao Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education National & Local Joint Engineering Research Center of Satellite Geospatial Information Technology Fuzhou University College of Zijin Mining Fuzhou University Fuzhou P.R. China

It is a fast and effective method to evaluate the ecological environment of mining area using remote sensing technology. In this paper, Landsat series data from 1992 to 2017 were used to monitor the change of ecological environment of Zijin Mine, located in the west of Fujian province, China. After radiometric calibration, the normalized difference vegetation indexes (NDVI) of Landsat TM or OLI from 1992 to 2017 were calculated. Based on NDVI data, the change of vegetation coverage of environment around Zijin mountain gold-copper deposit was analyzed. The results indicated that: (1)The area of Zijin mountain gold-copper deposit was increasing from 1992-2017. During 1996-2001, the area of mine was increasing rapidly because of large-scale blasting for removing loading strata in 1997, and it became obviously larger from 2014-2017 due to the copper price increases. (2)the NDVI around Zijin mountain raised from 1992 to 2014, indicating a better vegetation recovery, but it reduced from 2014 to 2017, indicating a worse vegetation recovery. low NDVI mainly exist in the middle of Zijin deposit, which is relate to mining activities. These results can give some scientific references for assessment of Zijin gold-copper deposit ecological environment.

关键词： Remote sensing Vegetation mapping data mining Earth Artificial satellites Copper Standards

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