  An increasing number of monitoring systems have been developed in smart
cities to ensure that real-time operations of a city satisfy safety and
performance requirements. However, many existing city requirements are written
in English with missing, inaccurate, or ambiguous information. There is a high
demand for assisting city policy makers in converting human-specified
requirements to machine-understandable formal specifications for monitoring
systems. To tackle this limitation, we build CitySpec, the first intelligent
assistant system for requirement specification in smart cities. To create
CitySpec, we first collect over 1,500 real-world city requirements across
different domains from over 100 cities and extract city-specific knowledge to
generate a dataset of city vocabulary with 3,061 words. We also build a
translation model and enhance it through requirement synthesis and develop a
novel online learning framework with validation under uncertainty. The
evaluation results on real-world city requirements show that CitySpec increases
the sentence-level accuracy of requirement specification from 59.02% to 86.64%,
and has strong adaptability to a new city and a new domain (e.g., F1 score for
requirements in Seattle increases from 77.6% to 93.75% with online learning).
