Abstract: A computer-implemented method for optimized decision making that includes labeling text data extracted from an inquiry, and linking labeled text to a knowledge graph entity. The method may further include retrieving from the knowledge graph reasoning paths; and removing irrelevant knowledge graph reasoning paths using a language model trained artificial intelligence consistent with the labeling of the text data. The method may further include employing remaining relevant graph reasoning paths to provide an answer prediction.

Abstract: Systems and methods for out-of-distribution detection of nodes in a graph includes collecting evidence to quantify predictive uncertainty of diverse labels of nodes in a graph of nodes and edges using positive evidence from labels of training nodes of a multi-label evidential graph neural network. Multi-label opinions are generated including belief and disbelief for the diverse labels. The opinions are combined into a joint belief by employing a comultiplication operation of binomial opinions. The joint belief is classified to detect out-of-distribution nodes of the graph. A corrective action is performed responsive to a detection of an out-of-distribution node. The systems and methods can employ evidential deep learning.

Abstract: Methods and systems for event detection include training a joint neural network model with respective neural networks for audio data and video data relating to a same scene. The joint neural network model is configured to output a belief value, a disbelief value, and an uncertainty value. It is determined that an event has occurred based on the belief value, the disbelief value, and the uncertainty value.

Abstract: Systems and methods for Evidence-based Sound Event Early Detection is provided. The system/method includes parsing collected labeled audio corpus data and real time audio streaming data utilizing mel-spectrogram, encoding features of the parsed mel-spectrograms using a trained neural network, and generating a final predicted result for a sound event based on the belief, disbelief and uncertainty outputs from the encoded mel-spectrograms.