Abstract: A system for managing personal data stored by an enterprise includes an interface, a permissions database, a processor, and non-transitory computer readable media. The interface is configured to receive a request to access at least some of the personal data, the request defining a purpose for the request. The permissions database that stores a plurality of records that define permissions associated with the personal data. The non-transitory computer readable media in communication with the processor that stores instruction code which, when executed by the processor, causes the processor to locate, within one or more disparate source databases within the enterprise, personal data associated with one or more individuals.

Abstract: Systems, methods, and apparatus, including computer programs encoded on computer storage media, for facilitating communication in an industrial control network. A system includes an industrial control network, one or more controller devices, one or more emulators, and an encryption relay processor. Each controller device can be operable to control one or more operational devices connected to the industrial control network. Each emulator can be configured to communicate with a respective controller device, and each emulator can be configured to reference a respective profile that includes information about security capabilities of the respective controller device. The encryption relay processor can be operable to facilitate communication to and from each emulator over the industrial control network.

Abstract: A device processes, with a model, an application to identify a set of file paths with process identifiers. The device identifies patterns associated with the set of file paths with process identifiers, and determines positions of random elements in each file path of the set of file paths with process identifiers. The device processes the patterns and the positions of the random elements to train a machine learning model, and utilizes the machine learning model to generate a first set of rules to identify files required for execution of the application, and a second set of rules to identify files not required for execution of the application. The device generates a mandatory access control policy based on the first set of rules and the second set of rules, and provides the mandatory access control policy to be implemented by an operating system of a client device.

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: In some examples, natural language eminence based robotic agent control may include ascertaining, by a robotic agent, an image of an object or an environment, and ascertaining a plurality of natural language insights for the image. For each insight of the plurality of insights, an eminence score may be generated, and each insight of the plurality of insights may be ranked according to the eminence scores. An operation associated with the robotic agent, the object, or the environment may be controlled by the robotic agent and based on a highest ranked insight.

Abstract: A data narration generating system generates snippets that include representations of data in one of a plurality of formats for inclusion into a data narration. The narration generating system receives selected data to be included into the data narration and provides the selected data to a plurality of ML models. The plurality of ML models are trained in generating snippets in one of the plurality of formats which can include textual format and a tabular format. Snippets in graphical formats can also be generated by rule-based processes. A plurality of snippets are thus generated in one or more of the plurality of formats which can then be presented to a user for selection and inclusion into the data narration. Alternately, a subset of the plurality of snippets can also be selected automatically based on a quality and quantity of data and a voting mechanism. The data narration thus generated is further configured to present different views based on privileges associated with user profiles.

Abstract: A system adapted to receive a knowledge base, which may include drug data, human biological data, drug-drug interactions, protein-protein interactions, gene expression, protein and drug interaction data, genotypic information for cell lines, drug side effects, and disease classification labels. The system may generate a knowledge graph based on the knowledge base, and convert the knowledge graph into embeddings that include points in a k-dimensional metric space. The system may determine a medical effect weighting based on a drug combination query, and update the embeddings of the drug combination. The system may utilize a pooling method to update predicate embeddings. The system may determine polypharmacy scores for the embeddings, and rank the predicted links between a drug combination and side effects.

Abstract: Examples of a cognitive forecasting system are defined. In an example, the system receives a forecasting requirement from a user. The system obtains parameter data from a plurality of data sources associated with the forecasting requirement and identify a parameter set therein. The system implements an artificial intelligence component to sort the parameter data into a plurality of data domains and identify a set of preponderant data domains therein. The system may update the preponderant data domains based on a modification in the plurality of data domains. The system may establish a forecasting model corresponding to the forecasting requirement by performing a cognitive learning. The system may update the forecasting model corresponding to the update in the parameter data. The system may generate a forecasting result corresponding to the forecasting requirement. The system may generate the cognitive forecasting model that may account for real time fluctuations in the data.

Abstract: An image-based product classification and recommender system employs a machine learning (ML) model for analyzing images for providing relevant recommendations to the users. An input image received from a user device is analyzed by the model for extraction of the image features that correspond to various attributes of a product in the image. A first subset of the image features is initially extracted and then applied to the input image to extract a next set of image features. The output from the model is then used for identifying products that match the user-selected product in the input image. The image-based product classification and recommender system also categorizes products in received images based on product attributes identified from the received images.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for a system to create and employ associative memory maps for analysis of security file and/or logs are disclosed. In one aspect, a method includes the actions of receiving, from an external application, a request for a recommended action; extracting information regarding the entities and relationships between the entities from a data source; constructing an associative memory map from the extracted information; selecting a subgraph from the associative memory map based on a result of employing a vector to search nodes in the associative memory map; identifying the nodes most relevant to the requested recommend action base on a shortest paths of traversal in the selected subgraph of nodes; determining the requested recommended action based on an event identified in the relationships between the identified most relevant nodes; and transmitting the recommended action to the external application.

Abstract: Implementations are directed to methods for detecting and identifying advanced persistent threats (APTs) in networks, including receiving first domain activity data from a first network domain and second domain activity data from a second network domain, including multiple alerts from the respective first and second network domains and where each alert of the multiple alerts results from one or more detected events in the respective first or second network domains. A classification determined for each alert of the multiple alerts with respect to a cyber kill chain. A dependency is then determined for each of one or more pairs of alerts and a graphical visualization of the multiple alerts is generated, where the graphical visualization includes multiple nodes and edges between the nodes, each node corresponding to the cyber kill chain and representing at least one alert, and each edge representing a dependency between alerts.

Abstract: A data platform may receive data files from an electronic data interchange (EDI). The data files may be received in multiple different data formats. The data platform may convert the data files to a common data format, extract data elements from the data files converted to the common data format, and assign the data elements extracted from the data files to file identifiers. The data platform may assign the data elements extracted from the data files to attribute identifiers that identify types of data represented by the data elements, aggregate the data elements to create a standardized data set, and map the data elements in the standardized data set to functions. The data platform may generate values based on mapping the data elements to the functions, determine a metric based on combining the values according to a metric definition, and post the metric to the EDI for consumption.

Abstract: A component analysis platform may communicate with one or more devices to obtain prediction data relating to a type of component. The component analysis platform may process the prediction data to determine a set of predictors for failure of an instance of the component, and may generate a model for failure of the instance of the component based on the set of predictors. The component analysis platform may monitor the instance of the component to obtain component data relating to the instance of the component. The component analysis platform may determine, using the model and based on the component data relating to the instance of the component, a predicted failure for the instance of the component. The component analysis platform may perform a response action related to the predicted failure.

Abstract: A data pipeline architecture is integrated with an analytics processing stack. The data pipeline architecture may receive incoming data streams from multiple diverse endpoint systems. The data pipeline architecture may include converter interface circuitry with multiple dynamic converters configured to convert the diverse incoming data stream into one or more interchange formats for processing by the analytics processing stack. The analytics processing stack may include multiple layers with insight processing layer circuitry above analysis layer circuitry. The analysis layer circuitry may control analytics models and rule application. The insight processing layer circuitry may monitor output from the analysis layer circuitry and generate insight adjustments responsive to rule changes and analytics model parameter changes produced at the analysis layer circuitry.

Abstract: Examples of a system for intelligent communication management for content summarization are provided. In an example, the system receives a summary generation requirement. The system establishes a question database for generation a set of questions for a user corresponding to the summary generation requirement. The system implements an artificial intelligence component to sort the answers obtained from the question and generates a user-specific knowledge database. The system may use the user-specific knowledge model for generation of further questions for the user. The system may assist a user with collecting information in a conversational manner mode and to automatically produce intelligible deliverables. The system may accept multiple modes of input while collecting information from a user. The system may be used for an automatic summary generation for customer service conversations, interviews, conferences and presentations, a person's holiday chronicles, and the like.

Abstract: The disclosure enables latent space exploration of a dataset based on drug molecular-structure data and drug biological-treatment data for a set of drug compounds in order to determine optimal drug compounds for treating diseases. Regional interpolation, including a linear interpolation (LERP) operation and a non-linear interpolation operation such as a spherical linear interpolation (SLERP), along with quantitative structure-activity relationship (QSAR) models may be utilized to navigate through a latent space generated from a variational auto-encoder (VAE), in accordance with certain embodiments.

Abstract: Systems and methods for active state synchronization between distributed ledger technology (DLT) platforms are provided. A system may store an origin blockchain compliant with an origin DLT. The system may further store a target blockchain compliant with a target DLT. The target DLT may be different from the origin DLT. The system may include a DLT object synchronizer with access to the origin blockchain and the target blockchain. The DLT object synchronizer may receive, from an exchange node, a request to synchronize an origin instance of a DLT object between the origin blockchain and the target blockchain. The DLT object synchronizer may select a target instance of the DLT object on the target blockchain. The DLT object synchronizer may format origin data from the origin instance for compliance with the target DLT. The DLT object synchronizer may synchronize the origin instance and the target instance.

Abstract: A system for providing integrated monitoring and communications of diagnostic equipment is disclosed. The system may comprise a data access interface, a processor, and an output interface. The data access interface may receive heterogeneous data from a plurality of machine and sensor equipment associated with performance of a system or product. The data access interface may also to receive a user inquiry pertaining to the system and product. The processor may generate a knowledge graph based on the data associated with the system or product, as well as convert the user inquiry into a knowledge graph query by: extracting entities from the user inquiry; extracting relations from the user inquiry to identify relationships between entities; expanding the user inquiry using the knowledge graph and the entities and relations; and translating the inquiry into knowledge graph triples.

Abstract: A data security and protection system that provides monitoring, diagnostics, and analytics within an enterprise network to identify potentially sensitive data is disclosed. The system may provide one or more data stores to store and manage personal data within a network. The system may also provide one or more servers to facilitate operations using information from the one or more data stores. The system may also provide an analytics system with processing components that determines uniqueness of personal data. The system may receive personal data and population attribute data via a data access interface. The analytics system may compare the data received to determine a fraction assignment, which when further processed using at least a combination or correlation technique, may yield a detailed uniqueness factor classification and analysis of the personal data to indicate its relative sensitivity.

Abstract: This disclosure relates to a development and application of a deep-learning neural network (DNN) model for identifying relevance of an information item returned by a search engine in response to a search query by a user, with respect to the search query and a profile for the user. The DNN model includes a set of neural networks arranged to learn correlations between queries, search results, and user profiles using dense numerical word or character embeddings and based on training targets derived from a historical search log containing queries, search results, and user-click data. The DNN model help identifying search results that are relevant to users according to their profiles.