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This paper describes the team peter-parker's participation in Hyperpartisan News Detection task (SemEval-2019 Task 4), which requires to classify whether a given news article is bias or not. We decided to use JAVA to do the article parser and the BERT model to do the bias analysis and prediction. Furthermore, we will show experiment results with analysis.

Purpose -This paper aims to design a vision-based non-contact real-time accurate heart rate (HR) measurement framework for home nursing assistant.Design/methodology/approach -The study applied Second-Order Blind Signal Identification (SOBI) algorithm to extract remote HR signal and analyzed it with Fast Fourier Transform (FFT). Multiple regions of interest are chosen and analyzed to obtain a more accurate result.Findings -An accurate non-contact hear rate (HR) measurement framework is proposed and proved to be efficient.Originality/value -The contributions of this HR measurement framework are as follows: accurate measurement of HR, real-time performance, robust under various scenes such as conversation, lightweight computation which is suitable and necessary for home nursing assistance. This framework is designed to be flexibly used in various real-life scenes such as domestic health assistance and affectively intelligent agents and is proved to be robust under such scenes.

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