Abstract: A system supports asset transfers among blockchains of differing distributed ledger technologies using interop circuitry. The interop circuitry may receive asset permissions from origin and target participant circuitry. The asset permissions may support transfer of an asset from an origin blockchain to a target blockchain. The interop circuitry, acting on behalf of the origin and target participant circuitry, locks an asset on the origin blockchain. Then the interop circuitry creates the asset on the target blockchain. The locking of the asset on the origin blockchain may prevent a double-expend opportunity, where the asset can be redeemed on the origin blockchain and on the target blockchain.

Abstract: The present disclosure provides a blockchain platform having functionality that enables blockchain nodes to be shared among multiple entities while maintaining security with respect to the blockchain. Blockchain platforms of embodiments include a hash function (also referred to as a chain-code) that enables attributes to be generated and attached to user identifiers, where the attributes associate users with specific entities supported by the blockchain. For shared nodes, the attribute may be utilized, at least in part, to control access permissions to data recorded to the blockchain. Embodiments of blockchain platforms may find particular utility when applied to complex use cases involving multiple entities.

Abstract: A device may receive a job request that requests performance of one or more operations by resources of a high-performance computing environment, and may process the job request, with a policy execution model trained with policy parameters, to identify policies to apply during execution of the job request. The device may process the job request, with a forecast object model trained with job data and profile data, to generate a forecast of resources and licenses required from the high-performance computing environment. The device may process the job request, other job requests, the one or more of the policies, and the forecast, with a heuristic model, to determine a schedule for the job request, and may process the schedule and current constraints on the resources and the licenses, with a linear programming model, to determine an optimized schedule for the job request.

Abstract: A system for solution architecture prediction may identify a previous solution from a data source and may create a historical solution evaluation matrix by mapping a plurality of concern categories with the previous solution. The system may identify a plurality of solution components preponderant to deriving a solution associated with the solution architecture prediction and create a potential solution evaluation matrix therefrom. The system may evaluate the historical solution evaluation matrix and the potential solution evaluation matrix to determine a credibility score for each solution comprised therein. Based on the evaluation, a solution prediction data may be generated including a previous solution, a potential solution, and the associated credibility score. A service solution may be selected from the solution prediction data to resolve the solution prediction requirement.

Abstract: A system supports symmetric release of cryptologically-locked asset transactions. A leading exchange party and a reciprocal exchange party establish, at least in part, a peer challenge in a pre-exchange proposal. The reciprocal party uses the peer challenge to lock a cryptologically-locked asset transaction. The solution to the peer challenge corresponds to an exchange key controlled by the leading exchange party. After establishment of the cryptologically-locked asset transaction, the leading party may request that exchange logic initiate release of the cryptologically-locked asset transaction. In response to the request, the exchange logic may execute a symmetric release of the exchange key and/or signature to the reciprocal exchange party and cryptologically-locked asset transaction (such that the asset is transferred to the leading exchange party).

Abstract: A system includes circuitry for rewriting blockchains in a non-tamper-evident or tamper-evident operation using a key secret held in portions by multiple individually untrusted parties. The blockchains may include a series of blocks secured by integrity codes that may prevent non-tamper-evident rewrites by non-trusted parties that are not in possession of the key secret or individually-untrusted parties in possession of only a portion of the key secret. In some cases, multiple individually-untrusted parties may combine their portions into the key secret. As a group, the multiple individually-untrusted parties may perform non-tamper-evident operation with respect to at least one integrity code within the blockchain.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for clustering data are disclosed. In one aspect, a method includes the actions of receiving feature vectors. The actions further include, for a subset of the feature vectors, accessing a first label. The actions further include generating a classifier that is configured to associate a given feature vector with a feature vector of the subset of the feature vectors. The actions further include applying the feature vectors that are not included in the subset of the feature vectors to the classifier. The actions further include generating a dissimilarity matrix. The actions further include, based on the dissimilarity matrix, generating a graph. The actions further include, for each node of the graph, determining a second label. The actions further include, based on the second labels and the first labels, determining a training label for each feature vector.

Abstract: A beverage dispensing system includes a nozzle assembly and a valve system, the nozzle assembly including a nozzle body having a diffuser chamber, a mixing chamber, and one or more cooling channels formed therein, a diffuser disposed within the diffuser chamber, and a manifold having one or more cooling channels formed therein and being configured to receive a flow of a base fluid and a flow of a concentrate for mixing of the base fluid and the concentrate in the mixing chamber to provide a beverage, and the valve system including one or more valves for regulating respective flow rates of each of the base fluid and the concentrate to substantially achieve a target mix ratio of the base fluid to concentrate within the mixing chamber.

Abstract: A device may receive, from a source system, data in a first format, data in a second format, or a combination of data in the first format and data in the second format. The device may identify a data format type, a hierarchy depth, levels, and objects at each of the levels for the data and may generate a template based on the data format type, the hierarchy depth, the levels, and the objects at each of the levels. The device may generate a query, for the data in the first format, based on the template and may execute the query, on the data in the first format, to generate query results associated with a target system. The device may process the query results, with the template, to generate output data in a third format associated with the target system and may perform actions based on the output data.

Abstract: The global proliferation of high speed communication networks has created unprecedented opportunities for geographically distributed resource interaction. A crowd sourced project management synthesizer provides crowd sourced project management features, as well as resource performance tracking features, to provide efficiencies to the project's implementation.

Abstract: A system and method for facilitating a rule-based anonymization of an original dataset is disclosed. The system may include a processor including a data privacy evaluator and a rules engine. The data privacy evaluator may receive at least one anonymized dataset corresponding to a predefined strategy of anonymization. The at least one anonymized dataset may include a variation from the original dataset by at least one of a privacy metric and a consistency metric. The data privacy evaluator may evaluate the at least one anonymized dataset and may generate a final output value based on a first output and a second output. The processor may assess the final output value with respect to a predefined threshold through the rules engine. If the final output value may be equal or higher than the predefined threshold, the system may permit an access to the anonymized dataset.

Abstract: An automatic artwork review system validates an artwork or a product label based on a received label specification document. Text extracted from the product label is chunked into sentences and words. Character-wise comparison is executed to identify the best match text from the label specification document for the sentence chunks from the product label. If the corresponding best match texts bears a similarity higher than a predetermined threshold to selected text including one or more sentence chunks, no errors are raised. If the similarity of the best match text to the selected text is not higher than the predetermined threshold, the specific errors occurring in the selected text and the particular portions where such errors are present are identified. The information regarding the errors can be output via one or more of an output user interface or a label compliance report.

Abstract: A device may receive customer data, and may identify unique communication channels associated with the customer data. The device may determine, based on the customer data, an optimal order for a Markov chain model, and may determine a model accuracy of the Markov chain model based on the optimal order. The device may transform transitions in the Markov chain model, based on the customer data, to generate transformed transitions, and may process the customer data, with a multi-level indexing model and based on the unique communication channels and the transformed transitions, to generate sparse matrices. The device may determine removal effects and steady state values for the sparse matrices, and may determine attribution weights for the unique communication channels based on the Markov chain model with the optimal order, the removal effects, and the steady state values. The device may perform actions based on the attribution weights.

Abstract: A device trains a classification model with defect classifier training data to generate a trained classification model and processes information indicating priorities and rework efforts for defects, with a Pareto analysis model, to select a set of classes for the defects. The device calculates defect scores for the set of the classes and selects a particular class, from the set of the classes, based on the defect scores. The device processes a historical data set for the particular class to identify a root cause corrective action (RCCA) recommendation and processes information indicating a defect associated with the particular class, with the trained classification model, to generate a predicted RCCA recommendation for the defect. The device processes the predicted RCCA recommendation and the RCCA recommendation, with a linear regression model, to determine an effectiveness score for the predicted RCCA recommendation and retrains the classification model based on the effectiveness score.

Abstract: A causal inference stack implements a targeted maximum likelihood scheme to conduct causal analysis of the observational data. At a data-handling layer, the causal inference stack obtains one or more memory locations for a dataset and establishes analysis nodes to setup localized data handling for the memory locations. At a data classification layer, the causal inference stack characterizes the missingness of the dataset. At a pipeline layer, the causal inference stack obtains a data element dependency query from a user and sets up an end-to-end solution path to determine the presence of a causal relationship between data elements identified in the data element dependency query.

Abstract: A multi-layer serverless sizing stack may determine a compute sizing correction for a serverless function. The serverless sizing stack may analyze historical data to determine a base compute allocation and compute buffer range. The serverless sizing stack may traverse the compute buffer range in an iterative analysis to determine a compute size for the serverless function to support efficient computational-operation when the serverless function is instantiated.

Abstract: An automatic image annotation system receives a reference image with one or more parts annotated along with one or more query images and automatically identifies portions from the query images that are similar to the annotated parts of the reference image. The S-matrices of the reference image and the query images are obtained via singular value decomposition (SVD). Lower-dimensional images are also obtained for the reference image and the query images using a pre-trained deep learning model. The S-matrices and the lower-dimensional images of the corresponding images are combined to generate vector representations. A distance metric is calculated for the vector representation of the reference image with that of the query image. A preliminary output image with a preliminary annotation is initially generated. The preliminary annotation is further optimized to generate an optimized annotation that adequately covers the region of interest (ROI).

Abstract: In some implementations, a control system may obtain historical data associated with usage of a distillation column during a historical time period. The control system may configure a prediction model to monitor the distillation column for a hazardous condition. The prediction model may be trained based on training data that is associated with occurrences of the hazardous condition. The control system may monitor, using the prediction model, the distillation column to determine a probability that the distillation column experiences the hazardous condition within a threshold time period. The prediction model may be configured to determine the probability based on measurements from a set of sensors of the distillation column. The control system may perform, based on the probability satisfying a probability threshold, an action associated with the distillation column to reduce a likelihood that the distillation column experiences the hazardous condition within the threshold time period.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for information extraction using natural language processing. One of the methods includes determining, for one or more tokens from a plurality of tokens that represent an unstructured sentence, a token type from a plurality of predetermined token types that indicates an element type for a phrase that corresponds to the token and has one or more properties using dependency data and a part-of-speech label for the token; assigning, for a token whose associated dependency data indicates that the token has a child, data for the child token to one of the one or more properties for the token type of the token; and providing, for use by a downstream semantic system and for the token, a textual representation of the phrase for the token and the phrases for one or more of the child tokens.

Abstract: Methods, systems, and computer-readable storage media for receiving a process aware AAG from computer-readable memory, the process aware AAG having been generated from the AAG, processing the process aware AAG to consolidate asset nodes to group nodes at least partially by providing metadata describing an asset node to a set of properties of a group node and pruning the asset node and any child nodes of the asset node from the process aware AAG, providing the aggregation graph by identifying relationships between group nodes and, for each relationship, inserting an edge between group nodes, and aggregating one or more of a set of node properties and a set of edge properties for each group node or edge, respectively, storing the aggregation graph to computer-readable memory, and executing one or more remedial actions in the enterprise network in response to analytics executed on the aggregation graph.