Current crowdsourcing platforms such as Amazon Mechanical Turk provide an attractive solution for processing of high-volume tasks at low cost. However, problems of quality control remain a major concern. We developed a private crowdsourcing system (PCSS) running in a intranetwork, that allow us to devise for quality control methods. In the present work, we designed a novel task allocation method to improve accuracy of task results in PCSS. PCSS analyzed relations between tasks from workers' behavior using Bayesian network, then created learning tasks according to analyzed relations. PCSS increased quality of task results by allocating learning tasks to workers before processing difficult tasks. PCSS created 8 learning tasks automatically for 2 target task categories and increased accuracy of task results by 10.77 point on average. We found that creating learning tasks according to analyzed relations is a practical method to improve the quality of workers.

Crowdsourcing platforms provide attractive solutions for collecting speech synthesis data for visually impaired person. However, quality control problems remain because of low-quality volunteer workers. In this paper, we propose the design of a crowdsourcing system that allows us to devise quality control methods. We introduce four worker selection methods; preprocessing filtering, real-time filtering, post-processing filtering, and guess-processing filtering. These methods include a novel approach that utilizes a collaborative filtering technique in addition to a basic approach involving initial training or use of gold-standard data. These quality control methods improved the quality of collected speech synthesis data. Moreover, we have already collected 140,000 Japanese words from 500 million web data for speech synthesis data.

The concept of a smart community has recently been attracting great attention as a means of utilizing energy effectively. One of the modules constituting the smart community is an intelligent transportation system, in which various sensors track movements of people and vehicles in real time to optimize migration pathways or means. Social media have the potential to serve as sensors, since people often post transportation information on such media. This paper presents a feasibility study on detecting information, focusing on train status information, by exploiting Twitter as a sensor. We dealt with two issues: (1) for the ambiguity of textual information expressed in tweets, we utilized heuristic rules in text manipulation, and (2) for the differences in the numbers of tweets among train lines, we optimized parameter values in statistical analysis for each train line. The experimental results show that the F-measure of detecting the information was more than 0.85 and the time taken to detect the information was less than 4 minutes. As a result we confirmed the high potential of detecting transportation information through Twitter.