Abstract: 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.

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

Abstract: A method includes obtaining, using at least one processor, audio/video content. The method also includes processing, using the at least one processor, the audio/video content with a trained attention-based machine learning model to classify the audio/video content. Processing the audio/video content includes, using the trained attention-based machine learning model, generating a global representation of the audio/video content based on the audio/video content, generating a local representation of the audio/video content based on different portions of the audio/video content, and combining the global representation of the audio/video content and the local representation of the audio/video content to generate an output representation of the audio/video content. The audio/video content is classified based on the output representation.

Abstract: 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.

Abstract: A method includes accessing video data that includes at least two different modalities. The method also includes using a convolutional neural network layer to incorporate temporal coherence into a machine learning model architecture configured to process the video data. The method further includes learning dependency among the at least two different modalities in an attention space of the machine learning model architecture. In addition, the method includes predicting one or more correlations among the at least two different modalities.

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

Abstract: A system and method to apply deep learning techniques to an automation engineering environment are provided. Big code files and automation coding files are retrieved by the system from public repositories and private sources, respectively. The big code files include examples general software structure examples to be utilized by the method and system to train advanced automation engineering software. The system represents the coding files in a common space as embedded graphs which a neural network of the system uses to learn patterns. Based on the learning, the system can predict patterns in the automation coding files. From the predicted patterns executable automation code may be created to augment the existing automation coding files.

Abstract: A method implemented by one or more computing systems includes accessing a set of content objects, in which each content object of the set of content objects is pre-labeled with concepts of a plurality of concepts organized according to a hierarchical relationship. The method further includes training, by a machine-learning model, a classification model for classifying content objects within the set of content objects. Training the classification model includes determining, for each object, a plurality of classification values corresponding to the plurality of concepts, calculating a loss for each of the plurality of classification values based on the pre-labeled concepts associated with the object, utilizing a hierarchical constraint loss function to calculate a maximum loss based on the calculated loss for each of the plurality of classification values, and updating the classification model based on the hierarchical constraint loss function until the maximum loss satisfies a predetermined criterion.

Abstract: 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. dynamic graph representation learning that includes receiving, by a processing device, graphs, each of the graphs having a known graph link between two vertices, each of the graphs being associated with one of a plurality of previous time steps.

Abstract: A computer-implemented method for prioritizing candidate objects on which to perform a physical process includes receiving a time series history of measurements from each of a plurality of candidate objects at a data processing framework. The method further includes reducing dimensionality of the time series history of measurements with a convolutional autoencoder to obtain latent features for each of the plurality of candidate objects. The method also includes applying a kernel regression model to the latent features to generate a predicted value of physical output for performing the physical process on each of the plurality of candidate objects. The method additionally includes generating a prioritization of the candidate objects based on the values of physical output. The method involves selecting fewer than all of the plurality of candidate objects on which to perform the physical process. The selected candidate objects are based on the prioritization.

Abstract: A computer-implemented method for prioritizing candidate objects on which to perform a physical process includes receiving a time series history of measurements from each of a plurality of candidate objects at a data processing framework. The method further includes reducing dimensionality of the time series history of measurements with a convolutional autoencoder to obtain latent features for each of the plurality of candidate objects. The method also includes applying a kernel regression model to the latent features to generate a predicted value of physical output for performing the physical process on each of the plurality of candidate objects. The method additionally includes generating a prioritization of the candidate objects based on the values of physical output. The method involves selecting fewer than all of the plurality of candidate objects on which to perform the physical process. The selected candidate objects are based on the prioritization.