Feng Wang

Lianmeng Jiao, Feng Wang, Zhun-ga Liu et al.

As a representative evidential clustering algorithm, evidential c-means (ECM) provides a deeper insight into the data by allowing an object to belong not only to a single class, but also to any subset of a collection of classes, which generalizes the hard, fuzzy, possibilistic, and rough partitions. However, compared with other partition-based algorithms, ECM must estimate numerous additional parameters, and thus insufficient or contaminated data will have a greater influence on its clustering performance. To solve this problem, in this study, a transfer learning-based ECM (TECM) algorithm is proposed by introducing the strategy of transfer learning into the process of evidential clustering. The TECM objective function is constructed by integrating the knowledge learned from the source domain with the data in the target domain to cluster the target data. Subsequently, an alternate optimization scheme is developed to solve the constraint objective function of the TECM algorithm. The proposed TECM algorithm is applicable to cases where the source and target domains have the same or different numbers of clusters. A series of experiments were conducted on both synthetic and real datasets, and the experimental results demonstrated the effectiveness of the proposed TECM algorithm compared to ECM and other representative multitask or transfer-clustering algorithms.

Jiangtao Wang, Feng Wang, Yasha Wang et al.

Task allocation is a major challenge in Mobile Crowd Sensing (MCS). While previous task allocation approaches follow either the opportunistic or participatory mode, this paper proposes to integrate these two complementary modes in a two-phased hybrid framework called HyTasker. In the offline phase, a group of workers (called opportunistic workers) are selected, and they complete MCS tasks during their daily routines (i.e., opportunistic mode). In the online phase, we assign another set of workers (called participatory workers) and require them to move specifically to perform tasks that are not completed by the opportunistic workers (i.e., participatory mode). Instead of considering these two phases separately, HyTasker jointly optimizes them with a total incentive budget constraint. In particular, when selecting opportunistic workers in the offline phase of HyTasker, we propose a novel algorithm that simultaneously considers the predicted task assignment for the participatory workers, in which the density and mobility of participatory workers are taken into account. Experiments on a real-world mobility dataset demonstrate that HyTasker outperforms other methods with more completed tasks under the same budget constraint.

Suncong Zheng, Feng Wang, Hongyun Bao et al.

Joint extraction of entities and relations is an important task in information extraction. To tackle this problem, we firstly propose a novel tagging scheme that can convert the joint extraction task to a tagging problem. Then, based on our tagging scheme, we study different end-to-end models to extract entities and their relations directly, without identifying entities and relations separately. We conduct experiments on a public dataset produced by distant supervision method and the experimental results show that the tagging based methods are better than most of the existing pipelined and joint learning methods. What's more, the end-to-end model proposed in this paper, achieves the best results on the public dataset.