Abstract: A system for predicting the occurrence of an event includes an event detector and a reporting processor. The event detector is configured to: receive data that defines a plurality of social media items; receive a real-time data feed; and predict the occurrence of an event based on a correlation between information in the plurality of social media items and activity associated with the real-time data feed. The reporting processor is configured to determine an event type associated with the event; identify a sentiment of the predicted event based on historical data in the real-time data feed, and generate a recommendation for preventing the occurrence of the event based on at least one of the event type and the sentiment of the predicted event. The recommendation includes a plurality of actions. The reporting processor is coupled to a knowledge graph database that corresponds to an ontology that defines one or more relationships between event types, and response types.

Abstract: A device may receive security data identifying assets of an entity, security issues associated with the assets, and objectives associated with the assets and may utilize a data model to generate, based on the security data, asset related data identifying mapped sets of security data. The device may process a first portion of the asset related data, with a first model, to calculate an asset risk likelihood score for an asset of the assets and may process a second portion of the asset related data, with a second model, to calculate an asset criticality score for the asset. The device may process a third portion of the asset related data, with a third model, to calculate an asset control effectiveness score for the asset and may combine the scores to generate a security risk score for the asset. The device may provide the security risk score for display.

Abstract: A system may receive events derived from historical sensor data. The system may generate repeat event records based on the events. The system may cluster the repeat event records into repeat event clusters. The system may determine filter ranges for the repeat event clusters. The system may generate a binarization filter comprising instructions to generate a binary sequence indicative of at least one of the filter ranges. The system may execute the binarization filter to generate binary sequences corresponding to the repeat event records, respectively. The system may train a prediction model based on the binary sequences. The system may deploy the binarization filter and the prediction model to a real-time platform.

Abstract: A device may receive security data identifying assets of an entity, security issues associated with the assets, and objectives associated with the assets and may utilize a data model to generate, based on the security data, asset related data identifying mapped sets of security data. The device may process a first portion of the asset related data, with a first model, to calculate an asset risk likelihood score for an asset of the assets and may process a second portion of the asset related data, with a second model, to calculate an asset criticality score for the asset. The device may process a third portion of the asset related data, with a third model, to calculate an asset control effectiveness score for the asset and may combine the scores to generate a security risk score for the asset. The device may provide the security risk score for display.

Abstract: In some implementations, a collaborative knowledge system may receive a first set and a second set of privatized embeddings. The first set of privatized embeddings may be generated by a local model based on a first set of private documents associated with a first knowledge domain. The second set of privatized embeddings may be generated by a local model based on a second set of private documents associated with a second, different knowledge domain. The collaborative knowledge system may train, based on the first and second sets of privatized embeddings, a centralized model. The collaborative knowledge system may receive a query associated with the first knowledge domain or the second knowledge domain. The collaborative knowledge system may generate a response to the query based on processing the query with the centralized model. The collaborative knowledge system may provide the response to the query to a user device.

Abstract: A device may generate, from a subset of historical ontology data and a substance description of a substance, a knowledge base. The subset of historical ontology data may be associated with historical substances. The device may generate, based on the knowledge base, a substance knowledge graph embedding (KGE) that is representative of the substance; compare the substance KGE and a historical KGE associated with the knowledge base; determine, based on comparing the substance KGE and the historical KGE, a similarity score associated with the substance KGE and the historical KGE; determine, based on the similarity score, whether substance data associated with a related substance is similarly represented in the substance KGE and the historical KGE; and perform, based on whether the substance data is similarly represented in the substance KGE and the historical KGE, an action associated with the related substance relative to the substance description or the knowledge base.

Abstract: A device may obtain configuration data for a server-side model. The device may generate synthetic usage data based on the configuration data. The device may train the server-side model based on the synthetic usage data. The device may determine update data based on the trained server-side model. The device may provide, to a client device, a message that includes the update data. The message may be configured to cause the client device to train a client-side model based on the update data and historical user data associated with the client device. The device may receive, from the client device, the client-side model data and may select content data based on the client-side model data and the trained server-side model. The device may provide the content data to the client device to permit the client device to provide content that is associated with the content data.

Abstract: A device may obtain configuration data for a server-side model. The device may generate synthetic usage data based on the configuration data. The device may train the server-side model based on the synthetic usage data. The device may determine update data based on the trained server-side model. The device may provide, to a client device, a message that includes the update data. The message may be configured to cause the client device to train a client-side model based on the update data and historical user data associated with the client device. The device may receive, from the client device, the client-side model data and may select content data based on the client-side model data and the trained server-side model. The device may provide the content data to the client device to permit the client device to provide content that is associated with the content data.

Abstract: In some implementations, a device may receive a first document from a first client device. The device may parse the first document into first sentences. The device may receive a second document from a second client device. The device may parse the second document into second sentences. The device may select a first sentence of the first document and a second sentence of the second document. The device may convert the first sentence into a first vector and the second sentence into a second vector. The device may determine a similarity score associated with the first vector and the second vector. The device may determine, based on the similarity score, that a first word set in the first sentence and a second word set in the second sentence correspond to a reference annotation. The device may perform an action associated with the reference annotation.

Abstract: A device may generate, from a subset of historical ontology data and a substance description of a substance, a knowledge base. The subset of historical ontology data may be associated with historical substances. The device may generate, based on the knowledge base, a substance knowledge graph embedding (KGE) that is representative of the substance; compare the substance KGE and a historical KGE associated with the knowledge base; determine, based on comparing the substance KGE and the historical KGE, a similarity score associated with the substance KGE and the historical KGE; determine, based on the similarity score, whether substance data associated with a related substance is similarly represented in the substance KGE and the historical KGE; and perform, based on whether the substance data is similarly represented in the substance KGE and the historical KGE, an action associated with the related substance relative to the substance description or the knowledge base.

Abstract: An image retrieval system may receive an image query that includes image data. The image retrieval system may determine an image descriptor based on the image data. The image retrieval system may obtain a text descriptor associated with the image descriptor in the descriptor repository. The image retrieval system may generate a document query comprising a search parameter, the search parameter including the text descriptor. The image retrieval system may identify, in a document database, text documents based on the document query. The text documents may be associated with document identifiers. The image retrieval system may obtain, from the file mapping repository, image identifiers associated with the document identifiers. The image query result from the image retrieval system may reference images associated with the image identifiers.

Abstract: Implementations of the present disclosure are generally directed to linking entities in dynamic knowledge graphs, and more particularly, to linking entities in knowledge graphs based on contextual evolution in one or more dimensions.

Abstract: An example method described herein involves receiving a data input; identifying a plurality of topics in the data input; determining an underrepresented set of data for a first set of topics of the plurality of topics based on a plurality of knowledge graphs associated with the first set of topics; calculating a score for each topic of the first set of topics based on a representative learning technique; determining that the score for a first topic of the first set of topics satisfies a threshold score; selecting a topic specific knowledge graph based on the first topic; identifying representative objects that are similar to objects of the data input based on the topic specific knowledge graph; generating representation data that is similar to the data input based on the representative objects to balance the underrepresented set of data with a set of data associated with a second set of topics of the plurality of topics; and performing an action associated with the representation data.

Abstract: A system may receive events derived from historical sensor data. The system may generate repeat event records based on the events. The system may cluster the repeat event records into repeat event clusters. The system may determine filter ranges for the repeat event clusters. The system may generate a binarization filter comprising instructions to generate a binary sequence indicative of at least one of the filter ranges. The system may execute the binarization filter to generate binary sequences corresponding to the repeat event records, respectively. The system may train a prediction model based on the binary sequences. The system may deploy the binarization filter and the prediction model to a real-time platform.

Abstract: A device may receive data associated with an event. The device may identify a context of the event based on receiving the data. The device may identify a similar event based on performing a comparison of the context of the event and a context of the similar event. The device may determine a set of pre-events associated with the event based on identifying a pre-event that occurred before the similar event. The set of pre-events may include at least one pre-event similar to the pre-event that occurred before the similar event. The device may determine a set of post-events associated with the event based on determining the set of pre-events and identifying a post-event that occurred after the similar event. The set of post-events may include at least one post-event similar to the post-event. The device may perform an action based on the set of post-events.

Abstract: According to an example, with respect to adverse information based ontology reinforcement, adverse information related to a product or a process may be ascertained and analyzed to further identify and ascertain a relevant ontology of a plurality of ontologies. A determination may be made as to whether the adverse information is present in the ascertained ontology, and if not, the adverse information may be integrated into the ascertained ontology to generate an updated ontology. Similar existing information corresponding to the ascertained adverse information may be identified in the updated ontology to determine an inconsistency between the identified similar existing information and the ascertained adverse information. The determined inconsistency may be used to modify the updated ontology to generate a reinforced ontology.

Abstract: A system for determining adverse effects associated with a substance includes a side-effect recognizer, a relationship extractor, a processor, and a reporting system. The side-effect recognizer utilizes a first recurrent neural network (RNN) to identify a first portion of received data associated with an adverse effect to thereby determine the adverse effect associated with the received data. The relationship extractor utilizes a second RNN to identify a second portion of the received data associated with a substance and a third portion of the received data that indicates a relationship between the substance and the adverse effect to thereby determine the substance associated with the received data and the relationship between the substance and the adverse effect. The processor is in communication with the adverse effect recognizer and the substance relationship extractor and aggregates and relates the adverse effect, substance, and relationship.

Abstract: A device obtains an image and generates, based on the image and using an image generation model, a set of candidate images for object recognition. The device determines, based on the set of candidate images and using an image evaluation model, that one or more candidate images, of the set of candidate images, are processed during image generation and using the image generation model to improve a resolution of the image. The device determines image embeddings for the one or more candidate images and based on the image, and determines an embeddings error for the one or more candidate images based on the image embeddings and the image. The device determines an identification of a feature of the image based on the image embeddings and the embeddings error, and performs an action relating to the identification of the feature based on determining the identification of the feature.

Abstract: An image retrieval system may receive an image query that includes image data. The image retrieval system may determine an image descriptor based on the image data. The image retrieval system may obtain a text descriptor associated with the image descriptor in the descriptor repository. The image retrieval system may generate a document query comprising a search parameter, the search parameter including the text descriptor. The image retrieval system may identify, in a document database, text documents based on the document query. The text documents may be associated with document identifiers. The image retrieval system may obtain, from the file mapping repository, image identifiers associated with the document identifiers. The image query result from the image retrieval system may reference images associated with the image identifiers.

Abstract: Implementations of the present disclosure are generally directed to linking entities in dynamic knowledge graphs, and more particularly, to linking entities in knowledge graphs based on contextual evolution in one or more dimensions.