Classification and Visualization of Crime-Related Tweets

Millions of Twitter posts per day can provide an insight to law enforcement officials for improved situational awareness. In this paper, we propose a natural-language-processing (NLP) pipeline towards classification and visualization of crime-related tweets. The work is divided into two parts. First, we collect crime-related tweets by classification. Unlike written text, social media like Twitter includes substantial non-standard tokens or semantics. So we focus on exploring the underlying semantic features of crime-related tweets, including parts-of-speech properties and intention verbs. Then we use these features to train a classification model via Support Vector Machine. The second part is to utilize visual analytics approaches on collected tweets to analyze and explore crime incidents. We integrate the NLP pipeline with Social Media Analytics Reporting Toolkit (SMART) to improve the accuracy of crime-related tweets identification in SMART. This paper can also be utilized to improve crime prediction for law enforcement personnel

Real-time Image Editing and iOS Application with Convolutional Networks

This thesis presents a new image editing approach with convolutional networks to automatically alter the image content with a desired attribute and still keep the image photo-realistic. The proposed image editing approach effectively combines the strengths of two prominent images editing algorithms, conditional Generative Adversarial Networks[16] and Deep Feature Interpolation[19], to be time-efficient, memory-efficient, and user-controllable. We also present an inverted deep convolutional network to facilitate the proposed image editing approach. Lastly, we describe the implementation of this image editing approach in an iOS application and demonstrate that this approach is feasible and practical in real-world applications