Learning representations for quality estimation of crowdsourced submissions

The problem of quality estimation of crowdsourced work is of great importance. Although a variety of aggregation methods have been proposed to find high-quality structured claims in multiple-choice crowdsourcing tasks such as item labeling, they do not apply to more general tasks, such as article writing and brand design with unstructured submissions. One possibility to tackle this problem is to ask another set of crowd workers to review and grade each submission, essentially transforming unstructured submissions into structured ratings. Nevertheless, such an approach incurs unnecessary monetary cost and delay. In this paper, we address this problem by exploiting task requesters’ historical feedback and directly modeling the submission quality. We propose two embedding-based methods where the first one learns worker embedding and the second one learns both worker embedding and meta information embedding, with additional consideration of neighborhood similarity. Experimental results on three large-scale crowdsourcing data sets demonstrate that our embedding-based feature-learning methods perform much better than feature-engineering methods that use popular learning-to-rank algorithms. At the same time, our methods do not require additional crowdsourced grading.     

材料物理专业实验室的建设与管理实践

结合材料物理专业实验教学改革情况,介绍了近年来我们实验室建设和管理的实践经验及取得的成效。总结了目前存在的问题并对未来的发展提出了建议。     

我国科技评估与评价实践的分析与探讨

运用评估手段为政府管理决策服务,是实现政府管理决策科学化的一项重要措施,几十年来已被世界上越来越多的国家采用,在发达国家已形成制度。分析我国科技评估与评价的发展与实践,将有助于我国科技评估制度和科技评估体系的建立。