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The role of the middle platform in digital government implementation

CHEN Bing, FANG Haibin, ZHAO Wenwen

Journal of East China Normal University(Natural Science) 2020, 2020 (5): 131-136. DOI: 10.3969/j.issn.1000-5641.202091008

Abstract （466）

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This paper introduces the characteristics of digital government by reviewing the development of digital government systems. Combined with the development of IT technology, it is demonstrated that the middle platform is an important technical support component for the construction of digital government. Using a case study on the building process of Shanghai’s “Integrated Online Platform”, this paper introduces the construction process of the middle platform on the business, data, and application aspects of government affairs; the article also offers a summary for the future direction of development.

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Methodology for building a business-oriented data asset system: Feature hierarchies

REN Yinzi

Journal of East China Normal University(Natural Science) 2020, 2020 (5): 137-145. DOI: 10.3969/j.issn.1000-5641.202091009

Abstract （1049）

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This paper proposes a new method for building a business-oriented data asset system. Data assets are one of the core components of the data middle office concept and require business-oriented asset mapping to realize the transformation from the asset to the broader business value. The proposed methodology uses feature hierarchies and describes how to organize data assets based on a tree structure, with the root as an object, the branch as a category, and a leaf/flower as a tag. There are energetic connections between the various object trees and the growth of these trees is supplied by the business. The instantiation of feature hierarchies can be realized in two modes: overall planning and local interception. The asset results are divided into two major parts, namely asset inventories and asset entities; these can be quickly configured as data service results for business use through service management tools in order to realize the value of the underlying data assets.

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Research on abnormal detection of daily loss rate based on a variational auto-encoder

ZHANG Guofang, LIU Tongyu, WEN Lili, GUO Guo, ZHOU Zhongxin, YUAN Peisen

Journal of East China Normal University(Natural Science) 2020, 2020 (5): 146-155. DOI: 10.3969/j.issn.1000-5641.202091013

Abstract （378）

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This paper adopts an anomaly detection algorithm based on a self-encoder to achieve anomaly detection of large-scale daily line loss rate data. A variational auto-encoder is a neural network that uses the backpropagation algorithm to make the output value approximately equal to the input value. It uses the auto-encoder to encode the original daily line loss rate time series and records the reconstruction possibility at each time point during the reconstruction process. When the reconstruction possibility is greater than a specified threshold, it is classified as anomaly data. In this paper, experiments were conducted on real daily line loss data. The test results show that the proposed algorithm for abnormal detection of daily line loss rate data based on an auto-encoder has good detection capability.

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GRS: A generative retrieval dialogue model for intelligent customer service in the field of e-commerce

GUO Xiaozhe, PENG Dunlu, ZHANG Yatong, PENG Xuegui

Journal of East China Normal University(Natural Science) 2020, 2020 (5): 156-166. DOI: 10.3969/j.issn.1000-5641.202091010

Abstract （472）

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There are generally two ways to realize most intelligent chat systems: ① based on retrieval and ② based on generation. The content and type of responses, however, are limited by the corpus chosen. The generative approach can obtain responses that are not in the corpus, rendering it more flexible; at the same time, it is also easy to produce errors or meaningless replies. In order to solve the aforementioned problems, a new model GRS (generative retrieval score) is proposed. This model can train the retrieval model and the generation model simultaneously. A scoring module is used to rank the results of the retrieval model and the generation model, and the responses with high scores are taken as the output of the overall dialogue system. As a result, GRS can combine the advantages of both dialogue systems and output a specific, diverse, and flexible response. An experiment on a real-world JingDong intelligent customer service dialogue dataset shows that the proposed model offers better outputs than existing retrieval and generation models.

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Merchant churn prediction based on transaction data of aggregate payment platform

XU Yiwen, LI Xiaoyang, DONG Qiwen, QIAN Weining, ZHOU Fang

Journal of East China Normal University(Natural Science) 2020, 2020 (5): 167-178. DOI: 10.3969/j.issn.1000-5641.202091016

Abstract （469）

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In the field of aggregate payments, ensuring a low dropout rate of merchants on the platform is a key issue to reduce the overall platform operating cost and increase profit. This study focuses on the prediction of merchant churn for aggregate payment platforms and aims to help the platform reactivate potential churn merchants. The paper proposes a series of features that are highly relevant to merchant churn and applies a variety of traditional machine learning models for prediction. Given that the data analyzed contains sequential information, the study, moreover, applies LSTM-based techniques to address the prediction problem. Experimental results on a real dataset show that the proposed features have a certain predictive ability and the results are interpretable. And, the LSTM-based approaches are capable of capturing the timing characteristics in the data and further improve prediction results.

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Discovering traveling companions using autoencoders

LI Xiaochang, CHEN Bei, DONG Qiwen, LU Xuesong

Journal of East China Normal University(Natural Science) 2020, 2020 (5): 179-188. DOI: 10.3969/j.issn.1000-5641.202091003

Abstract （350）

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With the widespread adoption of mobile devices, today’s location tracking systems are producing tremendous amounts of trajectory data on a continuous basis. The ability to discover moving objects that travel together (i.e., traveling companions) from their respective trajectories is desirable for many applications, including intelligent transportation systems and intelligent advertising. Existing algorithms are either based on pattern mining methods that define a particular pattern of traveling companions or based on representation learning methods that learn similar representations for similar trajectories. The former method suffers from the pairwise point-matching problem, and the latter often ignores the temporal proximity between trajectories. In this work, we propose a deep representation learning model using autoencoders, namely Mean-Attn (Mean-Attention) , for the discovery of traveling companions. Mean-Attn collectively injects spatial and temporal information into its input embeddings using skip-gram and positional encoding techniques, respectively. In addition, our model encourages trajectories to learn from their neighbors by leveraging the sort-tile-recursive (STR) algorithm as well as the mean operation and global attention mechanisms. After obtaining the representations from the encoder, we run DBSCAN (Density-Based Spatial Clustering of Applications with Noise) to cluster the representations and find traveling companions. Experimental results suggest that Mean-Attn performs better than the state-of-the-art data mining and deep learning algorithms for locating traveling companions.

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