Siemens Aktiengesellschaft

Arizona State University

University of Southern California

Palash Goyal

Arquimedes Martinez Canedo

Paulo Shakarian

Mohammed Almukaynizi

Kristina Lerman

Sujit Rokka Chhetri

Nazgol Tavabi

Di Huang

Applications of artificial intelligence (AI) in industrial automation have focused mainly on the runtime phase due to the availability of large volumes of data from sensors. Methods, systems, and apparatus that can use machine learning or artificial intelligence (AI) to complete automation engineering tasks are described herein.

Automation engineering learning framework for cognitive engineering

Methods and systems for dynamic network link prediction include generating a dynamic graph embedding model for capturing temporal patterns of dynamic graphs, each of the graphs being an evolved representation of the dynamic network over time. The dynamic graph embedding model is configured as a neural network including nonlinear layers that learn structural patterns in the dynamic network. A dynamic graph embedding learning by the embedding model is achieved by optimizing a loss function that includes a weighting matrix for weighting reconstruction of observed edges higher than unobserved links. Graph edges representing network links at a future time step are predicted based on parameters of the neural network tuned by optimizing the loss function.

Capturing network dynamics using dynamic graph representation learning

Various embodiments of a computer-implemented framework for predicting exploitation of software vulnerabilities are disclosed.

Systems and methods for a text mining approach for predicting exploitation of vulnerabilities

Growing community of inventors

Los Angeles, CA, United States of America

Palash Goyal