Abstract: The disclosed embodiments provide a system for processing data. During operation, the system obtains a feature configuration for a feature. Next, the system obtains, from the feature configuration, an anchor containing metadata for accessing the feature in an environment. The system then uses one or more attributes of the anchor to retrieve one or more feature values of the feature from the environment. Finally, the system provides the one or more feature values for use with one or more machine-learning models.

Abstract: A system for automatic incident response is disclosed. The system is programmed to receive information regarding an incident (event). The system is programmed to apply an action prediction model for inferring one or more programming actions from key phrases in an incident report, or apply a workflow prediction model for inferring one or more workflows of actions from an event descriptor. In response to receiving user input to modify a current workflow, the system is programmed to apply a workflow step prediction model for generating a recommended workflow from the modified workflow. The system is programmed to then apply a risk model for computing a risk score from the recommended workflow and user or environment information. The system is programmed to then transmit an alert or a confirmation depending on whether the risk score exceeds a threshold.

Abstract: A computer implemented method for displaying shoppable advertisements based on search query including receiving the search query from a user; retrieving an image comprising plurality of purchasable products related to the search query from a database; calculating a product relevance score for each of the plurality of products within each image based the search query; ranking each of the plurality products based on the product relevance score; selecting a subset of products based on the ranking; displaying the image comprising the plurality of products; and displaying the shoppable advertisements for the selected subset of products on a user device.

Abstract: A computer implemented method for displaying shoppable advertisements based on search query including receiving the search query from a user; retrieving an image comprising plurality of purchasable products related to the search query from a database; calculating a product relevance score for each of the plurality of products within each image based the search query; ranking each of the plurality products based on the product relevance score; selecting a subset of products based on the ranking; displaying the image comprising the plurality of products; and displaying the shoppable advertisements for the selected subset of products

Abstract: A computer implemented method for displaying shoppable advertisements based on search query including receiving the search query from a user; retrieving an image comprising plurality of purchasable products related to the search query from a database; calculating a product relevance score for each of the plurality of products within each image based the search query; ranking each of the plurality products based on the product relevance score; selecting a subset of products based on the ranking; displaying the image comprising the plurality of products; and displaying the shoppable advertisements for the selected subset of products on a user device.

Abstract: In an example, a piece of content is obtained. The piece of content is segmented into a plurality of segments. Each of the plurality of segments is divided into a plurality of units. Then, for each of the plurality of units for each segment, a quality model is used to pass the unit through a long short-term memory (LSTM) corresponding to the unit, causing an embedding of the unit and generating one or more parameters. For each of the plurality of segments for the piece of content, the quality model is used to pass the segment, and one or more parameters obtained from LSTMs corresponding to units within the segment, through an LSTM corresponding to the segment, causing an embedding of the segment. The piece of content is then classified as spam based on the embeddings of the plurality of segments for the piece of content, using the quality model.