Computational Finance and Business Intelligence (CFBI) Session 2

Time and Date: 15:45 - 17:25 on 11th June 2018

Room: M8

Chair: Yong Shi

305 Parallel Harris Corner Detection on Heterogeneous Architecture [abstract]
Abstract: Corner detection is a fundamental step for many image processing applications including image enhancement, object detection and pattern recognition. Recent years, the quality and the number of images are higher than before, and applications mainly perform processing on videos or image flow. With the popularity of embedded devices, the real- time processing on the limited computing resources is an essential problem in high-performance computing. In this paper, we study the parallel method of Harris corner detection and implement it on a heterogeneous architecture using OpenCL. We also adopt some optimization strategy on the many-core processor. Experimental results show that our parallel and optimization methods highly improve the performance of Harris algorithm on the limited computing resources.
Yiwei He, Yue Ma and Dalian Liu
307 A New Method for Structured Learning with Privileged Information [abstract]
Abstract: In this paper, we present a new method JKSE+ for structured learning. Compared with some classical mathods such as SSVM and CRFs, the optimization problem in JKSE+ is a convex quadratical problem and can be easily solved because it is based on JKSE. By incorporating the privileged information into JKSE, the performance of JKSE+ is improved. We apply JKSE+ to the problem of object detec- tion, which is a typical one in structured learning. Some experimental results show that JKSE+ performs better than JKSE.
Shiding Sun and Chunhua Zhang
312 An Effective Model between Mobile Phone Usage and P2P Default Behavior [abstract]
Abstract: P2P online lending platforms have become increasingly developed. However, these platforms may suffer a serious loss caused by default behaviors of borrowers. In this paper, we present an effective default behavior prediction model to reduce default risk in P2P lending. The proposed model uses mobile phone usage data, which are generated from widely used mobile phones. We extract features from five aspects, including consumption, social network, mobility, socioeconomic, and individual attribute. Based on these features, we propose a joint decision model, which makes a default risk judgment through combining Random Forests with Light Gradient Boosting Machine. Validated by a real-world dataset collected by a mobile carrier and a P2P lending company in China, the proposed model not only demonstrates satisfactory performance on the evaluation metrics but also outperforms the existing methods in this area. Based on these results, the proposed model implies the high feasibility and potential to be adopted in real-world P2P online lending platforms.
Huan Liu, Lin Ma, Xi Zhao and Jianhua Zou
340 A Novel Data Mining Approach towards Human Resource Performance Appraisal [abstract]
Abstract: Performance appraisal has always been a very important research field in human resource management. A reasonable performance appraisal plan lays a solid foundation for the development of an enterprise. Traditional performance appraisal programs are mostly labor-based, with difficulty in fairly examining employee results. Furthermore, as globalization and technology advance, enterprises meet fast changing strategic goals and increasing cross-functional tasks, which raises new challenges for performance appraisal. Starting from the above angles, this paper sets up a data mining-based performance appraisal framework, to conduct comprehensive assessment of employees on their ability to work and job competency. This framework has been successfully applied, providing a reliable basis for human resources management.
Pei Quan, Ying Liu and Yong Shi
341 Word Similarity Fails in Multiple Sense Word Embedding [abstract]
Abstract: Word representation is one foundational research in natu- ral language processing which full of challenges compared to other elds such as image and speech processing. It embeds words to a dense low- dimensional vector space and is able to learn syntax and semantics at the same time. But this representation only get one single vector for a word no matter it is polysemy or not. In order to solve this problem, sense information are added in the multiple sense language models to learn alternative vectors for each single word. However, as the most popular measuring method in single sense language models, word similarity did not get the same performance in multiple situation, because word simi- larity based on cosine distance doesn’t match annotated similarity scores. In this paper, we analyzed similarity algorithms and found there is ob- vious gap between cosine distance and benchmark datasets, because the negative internal in cosine space does not correspond to manual scores space and cosine similarity did not cover semantic relatedness contained in datasets. Based on this, we proposed a new similarity methods based on mean square error and the experiments showed that our new eval- uation algorithm provided a better method for word vector similarity evaluation.
Yong Shi, Yuanchun Zheng, Kun Guo, Wei Li and Luyao Zhu