Abstract: This document describes modeling and simulation techniques to select a cloud architecture profile based on correlations between application workloads and resource utilization. In some aspects, a method includes obtaining infrastructure data specifying utilization of computing resources of an existing computing system. Application workload data specifying tasks performed by one or more applications running on the existing computing system is obtained. One or more models are generated based on the infrastructure data and the application workload data. The model(s) define an impact on utilization of each computing resource in response to changes in workloads of the application(s). A workload is simulated, using the model(s), on a candidate cloud architecture profile that specifies a set of computing resources. A simulated utilization of each computing resource of the candidate cloud architecture profile is determined based on the simulation.

Abstract: A device may obtain information identifying a base application. The device may extract a set of sub-application artifacts associated with the base application based on structural information associated with the base application. The device may define a set of metadata attributes associated with the set of sub-application artifacts associated with the base application. The device may generate a set of hash tuples for the set of metadata attributes associated with the set of sub-application artifacts associated with the base application. The device may generate a base composite identity of the base application based on the set of hash tuples. The device may store the base composite identity in a blockchain and in connection with storage of the base application in the blockchain to enable subsequent identification and verification of the base application.

Abstract: Methods, systems, and computer-readable media for identifying potential process failure in a robotic process automation (RPA) platform, actions can include providing a predictive model based on application records of processes executed in the RPA platform, receiving data representative of a process being executed by a bot within the RPA platform, determining a prediction indicating a likelihood of failure of the process, and selectively issuing an alert in response to the prediction.

Abstract: A device may obtain, from a collection of data sources, personal information and activity information for a group of individuals. The device may generate profile information by associating the personal information and the activity information for each individual in the group of individuals. The device may determine a set of features capable of being used to train a set of machine learning models by using one or more feature identification techniques to analyze the profile information. The set of features may include generic features and contextualized features associated with the group of individuals. The device may train the set of machine learning models using one or more subsets of features of the set of features. The device may use the set of machine learning models to process a request from a client device for a list of prospective targets for a campaign.

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: This document described digital decoupling architectures that enable existing computing systems to run in parallel with new computing technologies. In some aspects, a method includes receiving, by a digital decoupling system and from a source computing system, one or more updated data sets that each include data that has been updated at the source computing system. A source data entry of a source table of a database of the digital decoupling system is updated based on each updated data set. In response to detecting the change to the source table, a target data entry that includes data of the updated source data entry is added to a target table of the database. An adapter module obtains the data of the target data entry and generates an event that specifies the data of the target data entry. The event is sent to one or more destination computing elements.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for automated software testing are disclosed. In one aspect, a method includes the actions of accessing plurality of test cases that each includes a plurality of test steps. The actions further include identifying a first group of modules that each include at least two sequential test steps. The actions further include comparing each module in the first group. The actions further include identifying a second group that each include the same first sequential test steps. The actions further include identifying a third group that each include the same first sequential test steps and the same second sequential test steps. The actions further include generating a fourth group. The actions further include selecting a subset of the fourth group. The actions further include updating the plurality of test cases by modularizing the test steps of the fourth group.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating rules to apply to fare transactions when a fare card reader cannot communicate with an authorization server, for generating historical data and a risk score for a fare card that are used by a fare card reader, with the rules, to determine whether to authorize access to a transit service when the fare card reader cannot communicate with the authorization server, and for selectively permitting or denying access to the transit service using the rules and the historical data or the score for a fare card.

Abstract: Systems and methods are provided to identify security vulnerabilities related to containerization platforms. Container images may be received from a repository, and scanned for security vulnerabilities. Containers may be automatically generated and updated with security updates when the images are extracted and identified. Updated versions of images may be generated based on the updated containers. Stored security vulnerability may be automatically updated with CVE information received from external databases at regular intervals, or upon receiving a scan request. Scan results may be generated, stored and compared. Vulnerability comparisons may be generated for an initial version of an image and an updated version of the image that includes the implemented security updates that rectify the identifiable security vulnerabilities.

Abstract: A device may receive data associated with a software development platform, and may correlate the data to generate correlated data. The device may train a first model, with the correlated data, to generate a software impact analyzer model, and may train a second model, with the correlated data, to generate a software development behavior model. The device may receive data identifying a new software requirement associated with the software development platform, and may process the data identifying the new software requirement, with the software impact analyzer model, to identify a file or a module impacted by the new software requirement. The device may process data identifying the file or the module, with the software development behavior model, to identify a developer to handle the new software requirement, and may perform one or more actions based on the data identifying the file or the module and data identifying the developer.

Abstract: A device may receive information associated with an entity. The information may include a first resource and a second resource. The first resource may be associated with a first file type, and the second resource may be associated with a second file type that is different than the first file type. The first resource may be associated with a first source, and the second resource may be associated with a second source that is different than the first source. The device may extract a plurality of attributes associated with the entity based on the information. The device may implement a natural language processing technique to extract the plurality of attributes. The device may associate the plurality of attributes with a plurality of elements based on extracting the plurality of attributes. The device may provide information that identifies the plurality of elements and the plurality of attributes to permit and/or cause an action to be performed.

Abstract: A system may receive video data generated by a camera device positioned in a physical environment. The system may receive electromagnetic (EM) response signals generated by an array of 5G antenna devices. The system may refine the EM response signals. The system may determine location information for a metallic object. The system may generate a movement trajectory of the metallic object. The system may generate a feature vector comprising an aggregation of the refined EM response signals and the movement trajectory. The system may submit the feature vector to a machine learning framework. The system may classify the metallic object as a weapon. The system may generate a virtual render comprising a graphical indication of the metallic object. The system may combine the virtual render with the video input data and display the combined virtual render and video data.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for accessing a database comprising multiple datasets. Each dataset includes data derived from a respective application. A machine-learning engine determines an analytical rule using at least one dataset of the multiple datasets. The analytical rule is determined by processing input data obtained from the at least one dataset derived from the respective application. A structured dataset is generated based on the determined analytical rule. The structured dataset is generated in response to using the determined analytical rule to analyze data from each dataset of the multiple datasets derived from the respective application. One or more data sequences that represent information flow of a transaction process are determined based on the structured dataset.

Abstract: A device may extract content, path data, and query data from API requests, and may identify events based on the extracted data. The device may combine a sequence of the events with the events to generate sequence-embedded events, and may train a neural network model with the sequence-embedded events to generate a trained neural network model. The device may receive an API request, may extract new content, new path data, and new query data from the API request, and may identify new events based on the newly extracted data. The device may process the new events, with the trained neural network model, to generate predicted events, and may calculate an anomaly score based on the predicted events. The device may compare the anomaly score with a threshold to determine a risk for the API request, and may cause the API request to be blocked or permitted based on the risk.

Abstract: Aspects of the present disclosure provide systems, methods, and computer-readable storage media that leverage artificial intelligence and machine learning to analyze and categorize events associated with an equipment asset, such as industrial machinery, to determine a status (e.g., insight) associated with the equipment asset, and to determine maintenance actions to be performed with respect to the equipment asset to prevent, or reduce the likelihood or severity of, occurrence of a fault at the equipment asset. Machine learning (ML) models may be trained to categorize events that are detected based on operating characteristics data associated with the equipment asset, to determine a status of the equipment asset, and to recommend one or more maintenance actions (or other actions). Output that indicates the maintenance actions may be displayed to a user or used to automatically initiate performance of one or more of the maintenance actions.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for service oriented software-defined security framework are disclosed. In one aspect, a system includes a security control device, one or more assets, and a security controller that communicates with the security control device and the one or more assets. The security controller includes a processing engine configured to register the security control device by creating a physical-logical attribute mapping for the security control device, and generating a security service description associated with the security control device. The processing engine is further configured to register the one or more assets by creating a physical-logical attribute mapping for each of the one or more assets, and generating security service requirements for each of the one or more assets. The processing engine is further configured to generate a security service binding based on a request for service.

Abstract: A system for generating an architecture diagram includes an input processor, a machine learning processor, and an advice generator. The input processor is configured to receive, from a terminal, entity data associated with a plurality of entities of an architecture and path data associated with a plurality of paths that correspond to interconnections between the plurality of entities. The machine learning processor utilizes a training dataset to assess whether the entities defined by the entity data are correctly interconnected as defined by the path data. The advice generator receives the assessment from the machine learning processor, prepares a recommendation based on the assessment, and communicates the recommendation to the terminal. User feedback is represented in the training data to improve the relevancy of the recommendation.

Abstract: A device may determine a behavioral pattern of an account over a past time period based on data relating to one or more transactions associated with the account. The device may identify one or more quantitative features of the behavioral pattern and one or more spatial features of the behavioral pattern. The device may determine an account type cluster to which the account belongs, based on the one or more quantitative features and the one or more spatial features identified. The device may determine, based on the account type cluster that is determined, a model for processing the behavioral pattern. The device may predict, using the model that is determined, an amount of funds that is likely to remain in the account during a future time period. The device may perform one or more actions based on the amount of funds that is predicted.

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 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. The cryptologically-locked asset transaction call for solution of the peer challenge and signature of the transaction by exchange logic as conditions of release of the cryptologically-locked asset transaction. 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).