Traffic simulation provides interactive data for the optimization of traffic control policies. However, existing traffic simulators are limited by their lack of scalability and shortage in input data, which prevents them from generating interactive data from traffic simulation in the scenarios of real large-scale city road networks.

Competitive programming has become a popular way for programmers to test their skills. Competition-level programming problems are challenging in nature, and participants often fail to solve the problem on their first attempt. Some online platforms for competitive programming allow programmers to practice on competition-level problems, and the standard feedback for an incorrect practice submission is the first test case that the submission fails. Often, the failed test case does not provide programmers with enough information to resolve the errors in their code, and they abandon the problem after making several more unsuccessful attempts.

Many existing systems for analyzing and summarizing customer reviews about products or service are based on a number of prominent review aspects.Conventionally, the prominent review aspects of a product type are determined manually.This costly approach cannot scale to large and cross-domain services such as Amazon.com,Taobao.com or Yelp.comwhere there are a large number of product types and new products emerge almost everyday.In this paper, we propose a novel framework, for extracting the most prominent aspects of a given product type from textual reviews.The proposed framework, ExtRA, extracts K most prominent aspect terms or phrases which do not overlap semantically automatically without supervision.Extensive experiments show that ExtRA is effective and achieves the state-of-the-art performance on a dataset consisting of different product types.