  Crowdsourcing is an online outsourcing mode which can solve the current
machine learning algorithm's urge need for massive labeled data. Requester
posts tasks on crowdsourcing platforms, which employ online workers over the
Internet to complete tasks, then aggregate and return results to requester. How
to model the interaction between different types of workers and tasks is a hot
spot. In this paper, we try to model workers as four types based on their
ability: expert, normal worker, sloppy worker and spammer, and divide tasks
into hard, medium and easy task according to their difficulty. We believe that
even experts struggle with difficult tasks while sloppy workers can get easy
tasks right, and spammers always give out wrong answers deliberately. So, good
examination tasks should have moderate degree of difficulty and
discriminability to score workers more objectively. Thus, we first score
workers' ability mainly on the medium difficult tasks, then reducing the weight
of answers from sloppy workers and modifying the answers from spammers when
inferring the tasks' ground truth. A probability graph model is adopted to
simulate the task execution process, and an iterative method is adopted to
calculate and update the ground truth, the ability of workers and the
difficulty of the task successively. We verify the rightness and effectiveness
of our algorithm both in simulated and real crowdsourcing scenes.
