Intelligent Automation, Inc.

Bluehalo Labs, LLC

Ziad El-Jamous

Yalin Evren Sagduyu

Yi Shi

Kemal Davaslioglu

Jason Li

Alexander Grushin

Min Ding

Vikram Manikonda

Wireless networks with plural nodes having a respective transceiver and a processor configured to, and methods to, obtain current state data, calculate a reward, store such state data and rewards in a collected parameters database, provide such current state data and data from such collected parameters database to a reinforced neural network, select an action using the reinforced neural network, and output the action to the respective transceiver so as to selective modify its transmit power level.

Systems and methods for deep reinforcement learning for optimal power control in wireless networks

A means and system is designed to distinguish human users from bots (automated programs to generate posts or interactions) in social media (including microblogging services and social networking services) by assigning a likelihood score to each user for being a human or a bot. The bot score assigned to each user is computed from statistical, temporal and text features that are detected in user's social media interactions (relative indicators specific to a given social media data set) and user's historical profile information.

System and means for detecting automated programs used to generate social media input

System and means generates synthetic forms of social media data such as data from microblogging services (e.g., Twitter) and social networking services (e.g., Facebook). This system and means jointly generate interaction graph structures and text features similar to input social media data. First, an interaction graph is generated by mapping social network interactions in input (real) social media data to graph structures. This interaction graph is fitted to a social network model (or a composite model) by minimizing the distance between the input and the synthetic interaction graphs (of potentially different sizes). The distance is measured statistically or based on the performance of social media analytics. Various patterns (such as anomalies), interaction types and temporal dynamics are generated synthetically. Second, text features are extracted from input social media data with topic modeling and statistical analysis of word tuple distributions. Based on these features, synthetic social media text is generated. Third, synthetic graph structures and text features are combined to generate the synthetic social media data. The system is particularly useful in generating data to be used for developing and testing new social media analytics or for generating or analyzing social bot network behavior and campaigns in social media, and for sharing test data with others without rate and privacy concerns.

System and means for generating synthetic social media data

Growing community of inventors

Rockville, MD, United States of America

Ziad El-Jamous