Group items tagged

"Amazon Rekognition to detect and recognize hundreds of thousands of individuals who are famous, noteworthy, or prominent in their field, from movies, television, politics, business, and sports. The Celebrity Recognition feature allows you to index and quickly search digital image libraries for celebrities based on your particular interest"

" A python application that detects and highlights the heart-rate of an individual (using only their own webcam) in real-time. "

"Interactive narrative systems attempt to tell stories to players capable of changing the direction and/or outcome of the story. Despite the growing importance of multiplayer social experiences in games, little research has focused on multiplayer interactive narrative experiences. We performed a preliminary study to determine how human directors design and execute multiplayer interactive story experiences in online and real world environments. Based on our observations, we developed the Multiplayer Storytelling Engine that manages a story world at the individual and group levels. Our flexible story representation enables human authors to naturally model multiplayer narrative experiences. An intelligent execution algorithm detects when the author's story representation fails to account for player behaviors and automatically generates a branch to restore the story to the authors' original intent, thus balancing authorability against robust multiplayer execution."

We show that easily accessible digital records of behavior, Facebook Likes, can be used to automatically and accurately predict a range of highly sensitive personal attributes including: sexual orientation, ethnicity, religious and political views, personality traits, intelligence, happiness, use of addictive substances, parental separation, age, and gender. The analysis presented is based on a dataset of over 58,000 volunteers who provided their Facebook Likes, detailed demographic profiles, and the results of several psychometric tests. The proposed model uses dimensionality reduction for preprocessing the Likes data, which are then entered into logistic/linear regression to predict individual psychodemographic profiles from Likes. The model correctly discriminates between homosexual and heterosexual men in 88% of cases, African Americans and Caucasian Americans in 95% of cases, and between Democrat and Republican in 85% of cases. For the personality trait "Openness," prediction accuracy is close to the test-retest accuracy of a standard personality test. We give examples of associations between attributes and Likes and discuss implications for online personalization and privacy.