No abstract

This paper introduces the Are u Depressed (AuD) model, which aims to detect depressive emotional intensity and classify detailed depressive symptoms expressed in user utterances. The study includes the creation of a BWS dataset using a tool for the Best-Worst Scaling annotation task and a DSM-5 dataset containing nine types of depression annotations based on major depressive disorder (MDD) episodes in the Diagnostic and Statistical Manual of Mental Disorders (DSM-5). The proposed model employs the DistilBERT model for both tasks and demonstrates superior performance compared to other machine learning and deep learning models. We suggest using our model for real-time depressive emotion detection tasks that demand speed and accuracy. Overall, the AuD model significantly advances the accurate detection of depressive emotions in user utterances.

Technology transfer becomes imperative in recent business environment where technology changes rapidly and its complexity becomes sophisticated. Among various context of technology transfer, it is especially important to predict patent transactions in such fast-changing industries. Therefore, this study aims to suggest a predictive model for patent transaction considering a wide range of decision variables. For this purpose, this study highlighted two considerations-technological impact of technology donor and technological proximity in previous patent transactions. Six factors are employed for developing our predictive model-technological strength, knowledge accumulation, technological protection scope, technological jurisdiction, technological strength of companies, and previous history of patent transfers. Five machine learning techniques are employed. As a result, we find that technological strength of companies and previous transfer history significantly affect technology transfer. This study is expected to be used in practice where the technology buying decision is very complicated and comprehensive.