There is a growing recognition that artists use valuable ways to understand and work with cognitive and perceptual mechanisms to convey desired experiences and narrative in their created artworks (DiPaola et al., 2010; Zeki, 2001). This paper documents our attempt to computationally model the creative process of a portrait painter, who relies on understanding human traits (i.e., personality and emotions) to inform their art. Our system includes an empathic conversational interaction component to capture the dominant personality category of the user and a generative AI Portraiture system that uses this categorization to create a personalized stylization of the user's portrait. This paper includes the description of our systems and the real-time interaction results obtained during the demonstration session of the NeurIPS 2019 Conference.

They took NYC Data Science Academy 12 week full time Data Science Bootcamp pr... between Sept 23 to Dec 18, 2015. The post was based on their third class project(due at 6th week of the program). Note: You'll find snippets of our code in the following post. For the full code please go to the github repository. Thanks to digital journalism, we have millions of opinions on practically every topic easily accessible at our fingertips.

They took NYC Data Science Academy 12 week full time Data Science Bootcamp pr... between Sept 23 to Dec 18, 2015. The post was based on their third class project(due at 6th week of the program). Note: You'll find snippets of our code in the following post. For the full code please go to the github repository. Thanks to digital journalism, we have millions of opinions on practically every topic easily accessible at our fingertips.

Researchers have recognized the importance of classifying personality through discourse for many years. However, this line of research tends to focus almost exclusively on the personality categories known as the Big Five factors. Though this information is certainly valuable, it may also be useful to categorize personality based on the Leary’s Interpersonal Circumplex model which emphasizes a predictive function. In this paper we construct the data set for personality annotation among six dimensions (based on a coding scheme developed from Leary’s Interpersonal Circumplex) for players using a chat interaction in an epistemic game, Land Science. Our results indicate that overall personality annotation is reliable (Average Kappa = 0.65) with the highest reliability for the competitive dimension and the lowest reliability for the leading dimension.