Abstract: A system may execute a pipelined multiple-tier test stack to support migration of computing resources via a migratory data stream. Via the pipelined multiple-tier test stack, the system may perform extract, transform, and load operations on the migratory data stream. The extract, transform, and load operations may be used to identify applications that may undergo testing. At a generation tier of the pipelined multiple-tier test stack, the system may generate test scripts, which may be used to test the application. The tests may be validated by the system via a validation tier of the pipelined multiple-tier test stack. To govern the operations, the pipelined multiple-tier test stack may rely on a multi-point reference data model.

Abstract: This disclosure relates to a system for managing personal data over a blockchain network. In an implementation, the system includes a user access provider (UAP) node in communication with a service provider (SP) node. The SP node may include an SP blockchain storing personal data on a SP transaction chain. The UAP node may store meta data of the user on a UAP transaction chain. The meta data may include access control information for the service provider to access the personal data. The UAP node may receive, from the SP node, an access request for the service provider to access a portion of the personal data of the user. The UAP node may access control information for the service provider based on the access request, update the meta data with the configured access control information, and replicate the meta data to the SP node via a blockchain transaction.

Abstract: A resource prediction system for executing machine learning models and method are provided. The system includes non-transitory memory storing instructions and a processor configured to execute the instructions to obtain input data including a targeted objective and the constraints, select a deployable machine learning model having an evaluation score that meets a predetermined criterion from among candidate machine learning models, virtually execute the deployable machine learning model on each of candidate hardware platforms according to the constraints, generate an assessment report of the virtual performance metrics set of the deployable machine learning model executed on each of the candidate hardware platforms, and select the suggested hardware platform meeting the predetermined criterion from among the candidate hardware platforms.

Abstract: A device receives, based on a user interaction with a chatbot, request data for a request for a service associated with a software tool that is part of a service management system. The device identifies, based on the request data, the service that is being requested and a first set of entity data for a first set of entities relating to the service. The device obtains a second set of entity data for a second set of entities by using an application programming interface (API) to reference a data structure that is associated with the service management system and that stores the second set of entity data. The device causes one or more components of the service management system to use the first set of entity data to execute a set of actions to perform the service.

Abstract: Implementations of the present disclosure include methods, systems, and computer-readable storage mediums for utilizing multiple AI service providers by a dialog management system. The dialog management system can include a dispatcher bot, multiple worker bots, and multiple AI adapters that are each associated with a different cloud-based AI service provider. In response to receiving a query, the dispatcher bot selects a particular worker bot to handle the query. The particular worker bot is assigned to a particular AI service provider. An AI adapter associated with the particular AI service provider, generates a query message based on the query. The AI adapter sends the query message to the particular AI service provider and receives a response message. The dialog management system sends a representation of the response message to the particular worker bot, receives an answer for the query from the particular worker bot, and provides the answer for output.

Abstract: The present system and method may generally include organizing the task flow of a virtual agent in a way that is controlled by a set of rules and set of conditional probability distributions. The system and method may include receiving a user utterance including a first task, identifying the first task from the user utterance, and obtaining a set of rules related to the plurality of tasks. The set of rules may determine whether pre-tasks and/or pre-conditions are to be executed before executing the first task. The set of rules may also determine whether post-tasks and/or post-conditions are to be executed after executing the first task. The system and method may include executing the task; running a probabilistic graphical model on the plurality of tasks to determine a second task based on the first task; suggesting to the user the second task; and updating the probabilistic graphical model after a threshold number of runs.

Abstract: A device may receive transaction data associated with transactions. The transaction data may be associated with transaction entries that are associated with the transactions. The device may process, using a matching model, the transaction entries to classify the transaction entries into a set of matched transaction entries and a set of unmatched transaction entries. The device may update a transaction grouping model based on the set of matched transaction entries to create an updated transaction grouping model. The device may determine, using the updated transaction grouping model, that a subset of the set of unmatched transaction entries are associated with a same transaction. The device may classify the subset of the set of unmatched transaction entries as grouped transaction entries. The device may provide an indication that the grouped transaction entries and the set of matched transaction entries are reconciled transactions.

Abstract: A device may receive data related to operations of an organization. The operations of the organization may be associated with a value. The data may be received from two or more systems associated with the organization. The device may process the data using a set of rules to permit an analysis of the data. The device may process the data to identify an error related to the data or a source of the error. The error may negatively impact the value associated with the operations of the organization. The device may perform an action after identifying the error or the source of the error. The action may facilitate fixing the error.

Abstract: A system and method for determining a result that includes a solution for a discrete non-linear optimization model for an optimization problem is disclosed. A non-linear relationship between a key performance indicator (KPI) to be optimized and at least one decision variable is determined using econometric modeling. A discrete optimization framework for the optimization problem is determined using a plurality of discrete values of decision variables of the optimization problem. A binary optimization framework is determined from the determined discrete optimization framework based on automatically revising an optimization equation of the discrete optimization framework. A continuous framework is determined from the determined binary optimization framework based on imposing additional constraints and based on modifying the optimization problem. The result is generated, and an action is controlled based on the result.

Abstract: A first device receives, via a blockchain node of the first device, data and constraints associated with a smart contract, where the smart contract is deployed via a blockchain node associated with a second device, the first and second devices are provided in a blockchain network, through their blockchain nodes. The first device provides, via the blockchain node, the data and constraints to an analytics engine of the first device, and performs, via the analytics engine, a data analytics technique on the data and constraints to generate an offer with optimized parameters. The first device provides the offer with the optimized parameters to the smart contract associated with the second device, and receives, via the blockchain node, a confirmation of a transaction associated with the smart contract. The first device causes the offer with the optimized parameters to be implemented based on receiving the confirmation of the transaction.

Abstract: A system and method of deploying and managing an integration for a hybrid computing environment is disclosed. The proposed systems and methods provide an intelligent hybrid cloud architecture and environment that offers reduced deployment times, and little to no errors. The system incorporates an artificial intelligence (AI) powered solution that is API-enabled and pre-integrated with system chatbots, as well as providing a secure, accelerated integration with available cloud ecosystems. The proposed solution is able to analyze business processes and derive and build deep insights toward the enterprise cloud integration, improving security, design, and performance of the hybrid architecture.

Abstract: A device may receive entity data identifying parameters needed by entities to generate a quotation, and entity encryption data identifying homomorphic encryption schemes supported by the entities. The device may receive, from a client device, a smart contract that includes a request for a quotation, client encryption data identifying homomorphic encryption schemes supported by the client device, and identification data identifying the entities. The device may encrypt the data included in the smart contract, based on the entity encryption data and the client encryption data, to generate encrypted data. The device may generate a modified smart contact by replacing the data included in the smart contract with references to the encrypted data. The device may provide the modified smart contract to server devices associated with the entities. The device may receive, from the server devices associated with the entities, the modified smart contract with quotations.

Abstract: This disclosure relates to industrial data services, data modeling and applications for controlling an industrial operation. In one implementation, a platform is disclosed for allocating a data modeling request to a collaborative group of experts based on a two-dimensional data modeling flow data structure and a multilayer resource allocation graph to obtain a data model for controlling the industrial operation. The two-dimensional data modeling flow data structure and the multilayer resource allocation graph are established from an industrial graph knowledgebase using various data analytics and machine learning techniques.

Abstract: A system includes a display control prime located within a central-zone in communication with a provisioned agent operating on an edge-zone device. The display control prime may implement prime-blind open-loop pixel-state control on the edge-zone device by executing pixel-state commands via the provisioned agent. The provisioned agent, operating within the edge-zone, may have access to un-sanitized data, which may be unavailable to the display control prime in the central-zone. The display control prime may provide conditional pixel-state commands via a pixel-state control parameter matrix. The provisioned agent may identify an operative pixel-state command from among the conditional pixel-state commands based on the un-sanitized data to which the display control prime is blind.

Abstract: Methods, systems, and apparatuses, including computer programs encoded on a computer storage medium, for facilitating analyzing media items and to filter inappropriate media items before distribution to the users. In one aspect, a method includes partitioning digital media items such as videos into segments and/or scenes, and classifying the segments into predetermined classes such as “Violence”, “Conversation”, “Street”, “Nudity”, “Animation”. After classifications have been assigned, the segments are clustered and/or grouped together before presenting the segments belonging to a particular cluster to a rating entity in a single user interface, for further evaluation. After evaluation, the segments of the media items that were approved by the rating entity are used to identify media items for which all the segments were approved by the rating entity before distributing the media items to the users.

Abstract: Implementations include evaluating a first sub-set of rules based on a first sub-set of facts to provide a first set of impacts, evaluating including applying the first sub-set of facts to each rule using a hash join operation to determine whether a rule results in an impact, indexes of arguments of facts being used in a probe phase of the hash join operation, evaluating a second sub-set of rules using impacts of the first set of impacts to provide a second set of impacts, determining whether each goal in a set of goals has been achieved using the first set of impacts and the second set of impacts, each goal being provided as an impact, in response to determining that each goal in the set of goals has been achieved, removing paths of the AAG, each of the paths resulting in an impact that is not a goal.

Abstract: An iterative classifier for unsegmented electronic documents is based on machine learning algorithms. The textual strings in the electronic document are segmented using a composite dictionary that combines a conventional dictionary and an adaptive dictionary developed based on the context and nature of the electronic document. The classifier is built using a corpus of training and testing samples automatically extracted from the electronic document by detecting signatures for a set of pre-established classes for the textual strings. The classifier is further iteratively improved by automatically expanding the corpus of training and testing samples in real-time when textual strings in new electronic documents are processed and classified.

Abstract: A device may communicate with a first device to receive first data. The device may communicate with a second device to receive second data. The device may combine the first data and the second data into a user interface. The user interface may provide a single location for displaying communications with the set of entities and with a system associated with the set of entities. The user interface may include a first section that provides information that is persistently presented in the user interface. The user interface may include a second section that provides information that is selectively presented in the user interface. The device may provide the user interface for display.

Abstract: A virtual agent with a dialogue management system and a method of training the dialogue management system is disclosed. The dialogue management system is trained using a deep reinforcement learning process. Training involves obtaining or simulating training dialogue data. During the training process, actions for the dialogue management system are selected using a Deep Q Network to process observations. The Deep Q Network is updated using a target function that includes a reward. The reward may be generated by considering one or more of the following metrics: task completion percentage, dialogue length, sentiment analysis of the user's response, emotional analysis of the user's state, explicit user feedback, and assessed quality of the action. The set of actions that the dialogue management system can take at any time may be limited by an action screener that predicts the subset of actions that the agent should consider for a given state of the system.

Abstract: The Next Best Action (NBA) management system and method is a real time decision solution usable in multiple industries, including Telco industry. The NBA system identify offers for call agents to pitch to customers, calculate and allow call center organizations and agents to view performance analytics, including an Inbound Campaign Agent Performance Index (ICAPI) for each agent. The NBA system uses usage oriented key performance indicators (KPIs) and sales oriented KPIs to add a weighted portion of the usage oriented KPIs and the sales oriented KPIs to calculate the ICAPI for each agent.