A method includes acquiring digital video data that portrays an interacting event, identifying a plurality of video features in the digital video data, analyzing the plurality of video features to create a relationship graph, determining a relationship score based on the relationship graph using a first computer-implemented machine learning model, and outputting the relationship score with a user interface. The interacting event comprises a plurality of interactions between a first individual and a second individual and each video feature of the plurality of video features corresponds to an interaction of the plurality of interactions. The relationship graph comprises a first node, a second node, and a first edge extending from the first node to the second node. The first node represents the first individual, the second node represents the second individual, and a weight of the first edge represents a relationship strength between the first individual and the second individual.

A method includes acquiring digital video data that portrays an interacting event, identifying a plurality of video features in the digital video data, analyzing the plurality of video features to create a relationship graph, determining a relationship score based on the relationship graph using a first computer-implemented machine learning model, and outputting the relationship score with a user interface. The interacting event comprises a plurality of interactions between a first individual and a second individual and each video feature of the plurality of video features corresponds to an interaction of the plurality of interactions. The relationship graph comprises a first node, a second node, and a first edge extending from the first node to the second node. The first node represents the first individual, the second node represents the second individual, and a weight of the first edge represents a relationship strength between the first individual and the second individual.

A graph processing method where the graph represents an image of a technical drawing including a view and a technical annotation. The method includes, for each view, providing the graph. The graph including nodes and edges. Each node corresponds to a classification of pixels into a semantic class of a predetermined set. Each edge connects two nodes either if the two nodes represent neighboring pixels or if the two nodes represent pixels distant from each other below a predetermined threshold. The set includes geometry, dimension, dimension-related symbol. The method includes clustering, based on the graph topology: nodes corresponding to the geometry class, to reconstruct the geometries in the view, and nodes corresponding to the dimension and dimension-related symbol classes, to reconstruct the annotations of the view. The method includes, associating reconstructed annotations to reconstructed geometries based on a detected position of the annotations and on the graph topology.

A graph processing method where the graph represents an image of a technical drawing including a view and a technical annotation. The method includes, for each view, providing the graph. The graph including nodes and edges. Each node corresponds to a classification of pixels into a semantic class of a predetermined set. Each edge connects two nodes either if the two nodes represent neighboring pixels or if the two nodes represent pixels distant from each other below a predetermined threshold. The set includes geometry, dimension, dimension-related symbol. The method includes clustering, based on the graph topology: nodes corresponding to the geometry class, to reconstruct the geometries in the view, and nodes corresponding to the dimension and dimension-related symbol classes, to reconstruct the annotations of the view. The method includes, associating reconstructed annotations to reconstructed geometries based on a detected position of the annotations and on the graph topology.

A method includes acquiring digital video data that portrays an interacting event, identifying a plurality of features in the digital video data, and analyzing the plurality of features to create a relationship graph. The relationship graph comprises a plurality of nodes and a plurality of edges, each node of the plurality of nodes represents an individual of the plurality of individuals, and each edge of the plurality of edges extends between two nodes of the plurality of nodes, and the plurality of edges represents a plurality of interactions of the interacting event. The method further includes identifying an edge of the plurality of edges as an anomalous edge, creating an output representative of the anomalous edge, and outputting the output representative of the anomalous edge. The anomalous edge is identified by a computer-implemented machine learning model configured to identify anomalous edges in relationship graphs.

A method includes acquiring digital video data that portrays an interacting event, identifying a plurality of features in the digital video data, and analyzing the plurality of features to create a relationship graph. The relationship graph comprises a plurality of nodes and a plurality of edges, each node of the plurality of nodes represents an individual of the plurality of individuals, and each edge of the plurality of edges extends between two nodes of the plurality of nodes, and the plurality of edges represents a plurality of interactions of the interacting event. The method further includes identifying an edge of the plurality of edges as an anomalous edge, creating an output representative of the anomalous edge, and outputting the output representative of the anomalous edge. The anomalous edge is identified by a computer-implemented machine learning model configured to identify anomalous edges in relationship graphs.

A method of detecting stealthy bitstreams in field programmable gate arrays (FPGAs) includes receiving an FPGA bitstream for configuring an FPGA; converting the FPGA bitstream into images; generating a graph from the images using a similarity evaluation; and performing a classification of the FPGA bitstream as benign or malicious using the graph as input to a graph convolutional network.

A method of detecting stealthy bitstreams in field programmable gate arrays (FPGAs) includes receiving an FPGA bitstream for configuring an FPGA; converting the FPGA bitstream into images; generating a graph from the images using a similarity evaluation; and performing a classification of the FPGA bitstream as benign or malicious using the graph as input to a graph convolutional network.

An artificial intelligence-based method for interpreting Piping and Instrumentation Diagram (P&ID) sheets is disclosed. The method includes obtaining a plurality of P&ID sheets in digital format and localizing symbols therein by generating bounding boxes. The localized symbols are labeled as a single generic class to generate a training dataset. A self-supervised learning process trains an artificial intelligence model using the training dataset to identify distinctive symbol features by minimizing the distance between embeddings of similar symbols while maximizing the distance between dissimilar ones. The trained model generates predictive output describing symbols in new P&ID sheets not used in training. The predictive output is then presented for further use.

An artificial intelligence-based method for interpreting Piping and Instrumentation Diagram (P&ID) sheets is disclosed. The method includes obtaining a plurality of P&ID sheets in digital format and localizing symbols therein by generating bounding boxes. The localized symbols are labeled as a single generic class to generate a training dataset. A self-supervised learning process trains an artificial intelligence model using the training dataset to identify distinctive symbol features by minimizing the distance between embeddings of similar symbols while maximizing the distance between dissimilar ones. The trained model generates predictive output describing symbols in new P&ID sheets not used in training. The predictive output is then presented for further use.