Abstract: Systems and methods for identifying item substitutions. History information can be collected. The history information can be transformed into a matrix of observed substitutions. A neural network can be trained on the matrix of observed substitutions to generate item embeddings. A substitution similarity between the item and another item based on the item embeddings can be identified.

Abstract: Methods, systems, and computer storage media for providing a data analytics index associated with a crisis-recovery data analytics engine in a data analytics system. The data analytics index is a consolidated single index representation of a set of variables associated with a set of consumer behaviors. The crisis-recovery data analytics engine supports generating the data analytics index associated with a pre-crisis period and a crisis-recovery period. In operation, a crisis-recovery dataset—associated with a set of variables of a set of consumer behaviors that support quantifying recovery from a crisis event—is accessed. The set of consumer behaviors are selected based on a crisis-recovery machine learning model that is trained on a pre-crisis dataset and a crisis dataset for selecting the set of consumer behaviors. A data analytics index is generated based on the crisis-recovery dataset. A data visualization comprising crisis-recovery data indicating recovery from the crisis event is generated.

Abstract: Systems and methods for optimizing cyber security projects. A method may include identifying cyber security related projects used to protect entity assets; and solving an optimization programming problem algorithm, using an optimization engine comprising an evolutionary algorithm, to identify project synergies to select a subset of cyber security related projects that optimize expected loss reduction from cyber security related threat.

Abstract: Methods, systems, and computer storage media for providing a unified multilayer-based index for a contextual geoanalytics engine in a data analytics system. The contextual geoanalytics engine is configured to aggregate point-of-interest geographical data from multiple data sources into an aggregate or composite dataset. The contextual geoanalytics engine then transforms and maps the data into a homogenous dataset—i.e., a location embedding record that is homogenous representation of an aggregated dataset—comparable across global geographical regions. The homogenous dataset is accessible via the unified multilayer-based index that is a single geographical index, where the homogenous dataset is a composite of different datasets. The data includes different data types, where the data types are stored in different layers while sharing a common index (i.e., unified multilayer-based index).

Abstract: Methods, systems, and computer storage media for providing a blending flow configuration for a material processing system that blends a material from multiple sources. A blending flow configuration identifies an arrangement of components and settings of the components in the material processing system to support blending a material. The blending flow configuration can support optimizing outcomes of different types of downstream processes. The material properties data are identified based on different types of measurements. For example, block models, lab assays, and on-stream analyzers can be used to determine a composition of the material. Grinding line performance data (or grinding line operation data) that estimates the grinding line performance or capacity can also be accessed. A description of a conveyance network design of the material processing system is generated.

Abstract: A largely automated method of categorizing spend data is provided that does not require a prior in-depth knowledge of an organization's transactional data. Natural language processing is applied to text data from transactional data to generate a consolidated cleaned data set (CDS) containing information for categorization. Logs for transactions are clustered based on similarity, forming the minimal data set (MDS). An automated algorithm selects a subset of high-value clusters that are categorized by requesting users to manually categorize one or more representative logs from each cluster of the subset. A model is then trained using the subset of manually categorized clusters and used to predict spend categories for the remaining logs with high accuracy. The AI engine automatically analyzes the predictions based on client context and either auto-tunes the machine learning model or identifies a new subset of clusters to be manually categorized. This loop may continue until 95%-100% of the spend is categorized.

Abstract: Methods, systems, and computer storage media for providing a broadband data analytics object for a geographical region using a broadband data analytics engine in a data analytics system. The broadband data analytics object can be a computational object that is a representation of broadband service data for a geographical region, the broadband service data is associated with features of broadband availability and broadband expansion targeting constraints (e.g., geocoding, structure type, unit counts, and speed). The broadband data analytics engine is configured to generate the broadband analytics object for the geographical region based on a combination of broadband data analytics operations, data structures (e.g. targeted-building information model), and datasets (e.g., geospatial datasets, parcel data).

Abstract: Systems and methods for identifying item substitutions. History information can be collected. The history information can be transformed into a matrix of observed substitutions. A neural network can be trained on the matrix of observed substitutions to generate item embeddings. A substitution similarity between the item and another item based on the item embeddings can be identified.

Abstract: Methods, systems, and computer storage media for providing an optimal control configuration for a material processing system are provided. In operation, a material processing engine accesses causal graph input data. Causal graph input data includes input data of a continuous flow process. Based on the causal graph and the input data, a causal graph that aligns with do-calculus manipulations—associated with determining identifiable causal relationships corresponding to input materials of the continuous flow process—is generated. The causal graph is parsed based on the do-calculus manipulations to determine valid conditioning sets associated with estimating a causal impact on an optimization target. Based on the valid conditioning sets, an optimal control configuration comprising optimal control variable values is generated.

Abstract: Systems and methods are disclosed for displaying information related to items in a store. Stock keeping unit (SKU) information for items in the store and combination information indicating which SKUs were sold together can be determined.

Abstract: Methods, systems, and computer storage media for providing an error remediation recommendation—for flagged incorrect values in broadband service deployment data—using a broadband error remediation engine in a data analytics system. The error remediation recommendation can identify a corrective action to resolve errors (i.e., incorrect values) in broadband service deployment data. Operationally, the broadband error remediation engine is configured to access broadband service deployment data associated with a plurality of broadband service data features. The broadband error remediation engine uses an error remediation model to and a plurality of datasets to execute a sequence of error remediation operations (e.g., checks and comparisons). Based on executing the sequence of error remediation operations, a corrective action (e.g., change geographic coordinates or remove deployment units) for an incorrect value of a broadband service feature in the broadband service deployment data.

Abstract: Systems and methods for identifying item substitutions. History information can be collected. The history information can be transformed into a matrix of observed substitutions. A neural network can be trained on the matrix of observed substitutions to generate item embeddings. A substitution similarity between the item and another item based on the item embeddings can be identified.

Abstract: Methods and Systems for context-sensitive manipulation of an object via a presentation software are disclosed.

Abstract: Systems and methods for identifying item substitutions. History information can be collected. The history information can include one or more episodes from one or more customers. Each episode can include one or more items. The history information can be transformed into a matrix of observed substitutions. A neural network can be trained on the matrix of observed substitutions to generate item embeddings. Input including an item can be received. A substitution similarity between the item and another item based on the item embeddings can be identified.

Abstract: Systems and methods for managing data. Person and position data can be copied from a database and it can be determined whether a person is aligned to any positions. Information can be displayed representing the person and indicating any positions to which the person is aligned.

Abstract: A method for screening documents. The order of the text in the documents can be changed in order to create additional documents. A vocabulary of words from the text in the documents can be built. The words can be coded as vectors. Vector relationships can be defined. The documents can be classified using the vector relationships.

Abstract: Systems and methods for managing data. Person and position data can be copied from a database and it can be determined whether a person is aligned to any positions. Information can be displayed representing the person and indicating any positions to which the person is aligned.

Abstract: Methods and systems for determining relevance for a new document are described. Existing documents that have a high probability of relevance can be chosen. A vocabulary of words in the existing documents can be built. Each word can be mapped into a vector such that each existing document can be represented by a sequence of vectors and each sentence and/or paragraph in each existing document can be represented by a subsequence of vectors including a subset of the sequence of vectors. Data augmentation can be applied changing an order of the subsequences in order to create additional documents represented by the subsequences. A deep neural network can be trained using the subsequences that represent the existing documents and the subsequences that represent additional documents. The new documents can be trained using a trained deep neural network. A relevant document can be output using the trained deep neural network.

Abstract: Methods and systems of using reinforcement learning to optimizing promotions. A promotion can be offered to a user using a reinforcement learning model with a sensitivity parameter, the reinforcement module estimating a time period during which the user will respond to the first information. The user's reaction to the promotion can be observed. The reinforcement learning model can be adapted based on the user's reaction.

Abstract: Methods and systems for context-sensitive manipulation of an object via a presentation software are disclosed. A target object state can be obtained. An actual state of the object can be determined to be different from the target object state. The actual state of the object can be manipulated to synchronize with the target object state.