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The stock market is affected by a seemingly infinite number of factors, making it highly uncertain yet impactful. A large determinant of stock performance is public sentiment, which can often be volatile. To integrate human inputs in a more structured and effective manner, this study explores a combination of the wisdom of crowds concept and machine learning (ML) for stock price prediction. A crowdsourcing study is developed to test three ways to elicit stock predictions from the crowd. The study also assesses the impact of priming participants with estimates provided by a Long Short Term Model (LSTM) model herein developed for this context. 

Forensic application of automatically matching skull with face images is an important research area linking biometrics with practical applications in forensics. It is an opportunity for biometrics and face recognition researchers to help the law enforcement and forensic experts in giving an identity to unidentified human skulls. It is an extremely challenging problem which is further exacerbated due to lack of any publicly available database related to this problem. This is the first research in this direction with a twofold contribution: (i) introducing the first of its kind skullface image pair database, IdentifyMe, and (ii) presenting a preliminary approach using the proposed semi-supervised formulation of transform learning. The experimental results and comparison with existing algorithms showcase the challenging nature of the problem. We assert that the availability of the database will inspire researchers to build sophisticated skull-to-face matching algorithms.