The uncertainty measurement of classified results is especially important in areas requiring limited human resources for higher accuracy. For instance, data-driven algorithms diagnosing diseases need accurate uncertainty score to decide whether additional but limited quantity of experts are needed for rectification. However, few uncertainty models focus on improving the performance of text classification where human resources are involved. To achieve this, we aim at generating accurate uncertainty score by improving the confidence of winning scores. Thus, a model called MSD, which includes three independent components as "mix-up", "self-ensembling", "distinctiveness score", is proposed to improve the accuracy of uncertainty score by reducing the effect of overconfidence of winning score and considering the impact of different categories of uncertainty simultaneously. MSD can be applied with different Deep Neural Networks. Extensive experiments with ablation setting are conducted on four real-world datasets, on which, competitive results are obtained.

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The exponential growth of the urban data generated by urban sensors, government reports, and crowd-sourcing services endorses the rapid development of urban computing and spatial data mining technologies. Easier accessibility to such enormous urban data may be a double-bladed sword. On the one hand, urban data can be applied to solve a wide range of practical issues such as urban safety analysis and urban event detection. On the other hand, ethical issues such as biasedly polluted urban data, problematic algorithms, and unprotected privacy may cause moral disaster not only for the research fields but also for the society. This paper seeks to identify ethical vulnerabilities from three primary research directions of urban computing: urban safety analysis, urban transportation analysis, and social media analysis for urban events. Visions for future improvements in the perspective of ethics are addressed.

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