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Sharing bicycles, as boosted by the advanced mobile technologies, is expected to mitigate the traffic congestion and air pollution issues in China. A survey study was conducted with 335 valid samples to identify the key factors that influence the customers' intention of use for bike-sharing system and quantify the corre-sponding importance. Five machine learning techniques for classi-fication are applied and results are compared. The best performed technique is selected to prioritise and quantify the importance level of the influencing factors. The results indicate that the perceived ease of use is the most significant factor for the intention to use sharing bikes.

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Text normalization is an important component in mandarin Text-to-Speech system. This paper develops a taxonomy of Non-Standard Words (NSW's) based on a Large-scale Chinese corpus and proposes a three-stage text normalization strategy: Finite State Automata (FSA) for initial classification, Maximum Entropy (ME) Classifier & Rules for further classification and General Rules for standard word conversion. The three-stage approach achieves Precision of 96.02% in experiments, 5.21% higher than that of simple rule based approach and 2.21% higher than that of simple machine learning method. Experiments results show that the approach of three-stage disambiguation strategy for text normalization makes considerable improvement, and works well in real TTS system.