arXiv subjects

Dongyang Li

Publications and source records attributed to Dongyang Li.

57 records · Page 4Linked to original sources

arXiv2019

Prediction Modeling and Analysis for Telecom Customer Churn in Two Months

A practical churn customer prediction model is critical to retain customers for telecom companies in the saturated and competitive market. Previous studies focus on predicting churn customers in current or next month, in which telecom companies don't have enough time to develop and carry out churn management strategies. In this paper, we propose a new T+2 churn customer prediction model, in which the churn customers in two months are recognized and the one-month window T+1 is reserved to carry out churn management strategies. However, the predictions for churn customers in two months are much more difficult than in current or next month because of the weaker correlation between the customer information and churn states. Two characteristics of telecom dataset, the discrimination between churn and non-churn customers is complicated and the class imbalance problem is serious, are observed. To discriminate the churn customers accurately, random forest (RF) classifier is chosen because RF solves the nonlinear separable problem with low bias and low variance and handles high feature spaces and large number of training examples. To overcome the imbalance problem, synthetic minority over-sampling with borderline or tomek link, in which the distribution of the samples remains and the number of the training examples becomes larger, is applied. Overall, a precision ratio of about 50% with a recall ratio of about 50% is achieved in the T+2 churn prediction. The proposed prediction model provides an accurate and operable churn customer prediction model for telecom companies.

cs.IR↗

arXiv2019

Aurora Guard: Real-Time Face Anti-Spoofing via Light Reflection

In this paper, we propose a light reflection based face anti-spoofing method named Aurora Guard (AG), which is fast, simple yet effective that has already been deployed in real-world systems serving for millions of users. Specifically, our method first extracts the normal cues via light reflection analysis, and then uses an end-to-end trainable multi-task Convolutional Neural Network (CNN) to not only recover subjects' depth maps to assist liveness classification, but also provide the light CAPTCHA checking mechanism in the regression branch to further improve the system reliability. Moreover, we further collect a large-scale dataset containing $12,000$ live and spoofing samples, which covers abundant imaging qualities and Presentation Attack Instruments (PAI). Extensive experiments on both public and our datasets demonstrate the superiority of our proposed method over the state of the arts.

cs.CV↗

arXiv2015

Variation in electron work function with temperature and its effect on the Young's modulus of metals

Properties of metals are fundamentally determined by their electron behavior, which is largely reflected by the electron work function ($φ$). Recent studies have demonstrated that many properties of metallic materials are directly related to $φ$, which may provide a simple but fundamental parameter for material design. Since material properties are affected by temperature, in this article a simple model is proposed to correlate the work function with temperature, expressed as $φ(T)=φ_{0} -γ\frac{(k_{B} T)^{2}}{φ_{0}} $, where $γ$ varies with the crystal structure. This $φ$-T relationship helps determine and understand the dependence of metal properties on temperature on a feasible electronic base. As a sample application, the established relationship is applied to determine the dependence of Young's modulus of metals on temperature. The proposed relationship is consistent with experimental observations.

cond-mat.mtrl-sci↗