Abstract: A machine provides a system and interface to deploy and manage pre-defined analytical models across various compute engines or run time environments, e.g., by exposing analytical model deployment and management parameters to a user while abstracting model deployment activities. The machine also determines proper run time environments for the pre-defined analytical model and verifies the pre-defined analytical model. The machine also provides a dynamically reconfigurable user interface for controlling the machine.

Abstract: An analytics platform may generate one or more user interfaces based on the set of testing parameters. The analytics platform may provide the one or more user interfaces. The analytics platform may receive first test information corresponding to first respondents. The analytics platform may identify, based on the first test information and based on information identifying particular respondents of the first respondents, characteristic attributes of the particular respondents. The analytics platform may train, based on the values of the characteristic attributes and performance information associated with the first respondents, a predictive model. The analytics platform may obtain the second test information corresponding to the one or more second respondents. The analytics platform may determine the predicted performance information associated with the one or more second respondents using the predictive model. The analytics platform may perform an action based on the predicted performance information.

Abstract: Implementations are directed to detecting a fluid leak by performing operations including: capturing, by one or more digital cameras, at least one image, the one or more digital cameras being included in an imaging system attached to a vehicle moving between geographical locations, and the at least one image corresponding to a geographical location, generating, by one or more fluid detectors, spectral data indicative of at least one fluid at the geographical location, receiving from the one or more digital cameras the at least one image, and from the one or more fluid detectors, the spectral data of the fluid at the geographical location, and providing an output signal based on the at least one image and the spectral data, the output signal indicating a fluid leak.

Abstract: Methods and systems for a quantum computing approach to solving challenging, e.g., NP-complete, problems in transportation. One of the methods includes (a) ingesting transportation-related data into a graph structure, the transportation-related data being associated with a transportation system; (b) identifying a transportation metric associated with the transportation system; (c) identifying at least one attribute associated with the transportation-related data, where the transportation metric is based at least in part on the attribute; (d) using a quantum computer to derive an operational parameter for the attribute that improves the transportation metric; and (e) applying the operational parameter to the operation of the transportation system.

Abstract: A system may facilitate a distributed ledger technology (DLT) record based (for example, blockchain-based) auditing. A node configured to support an auditor role may access a ballot to determine answers and one or more voter transactions to determine committed tokens conferred to the answers. The auditor may access summation information for the committed tokens to facilitate comparison to a voter token acting as a source token for the committed tokens. Without necessarily gaining knowledge of the vote-value conferred by the individual committed tokens, the auditor may compare the committed tokens as a sum to the voter token and confirm that the sum of the committed tokens is consistent with the voter token. The auditor may generate an indication of the result of the comparison.

Abstract: Implementations are directed to providing categorization of transactional data, and include actions of providing a plurality of word embeddings based on domain-relevant text data, clustering word embeddings of the plurality of word embeddings into a plurality of clusters, receiving, in real-time, transactional data representative of a transaction, providing a category that is to be assigned to the transaction based on the transactional data, and the plurality of clusters, processing the category, the transactional data, the text data, and the plurality of clusters using a semantic search to provide reason text data, the reason text data representing a reason for selection of the category assigned to the transaction, and storing the transaction data, the category, and the reason text data in a transaction database.

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: According to an example, with respect to artificial intelligence based service implementation, a voice call may be analyzed to generate voice data. The voice data may be converted to text data, which may be analyzed to identify keywords. Based on an analysis of the identified keywords, a user of a plurality of users may be identified. A user assistance flow of a plurality of user assistance flows that corresponds to a determined intent of the identified user may be ascertained. The voice call may be transferred to a digital assistant that may provide artificial intelligence based assistance to the identified user based on the user assistance flow that corresponds to the determined intent.

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: A system may comprise one or more processors. The one or more processors may store data, associated with a plurality of documents, as stored data and may receive documents from a plurality of client devices. The documents may include a plurality of different types of data. The one or more processors may assign the documents to one or more groups based on the plurality of different types of data and may identify a group of documents of the one or more groups. The one or more processors may compare data, within documents in the group of documents, to the stored data and may transmit, to one or more devices, a result of comparing the data to the stored data.

Abstract: A first device may receive data from a set of second devices to be processed to determine a quality of the data. The data may include first data stored by the set of second devices, second data provided toward a third device, or third data related to fourth data. The first device may process the data using a first set of techniques to prepare the data for processing. The first device may process the data using a second set of techniques to improve the quality of the data and to form processed data. The first device may provide the processed data toward the set of second devices to replace the data stored by the set of second devices to permit the set of second devices to use the processed data. The first device may perform an action after providing the processed data toward the set of second devices.

Abstract: A device may receive information that identifies an item to be categorized. The device may map the item to a first vector based on the information that identifies the item. The device may compare the first vector to a second vector based on mapping the item to the first vector. The device may determine a similarity value between the first vector and the second vector based on comparing the first vector and the second vector. The device may determine that the similarity value satisfies a threshold. The device may determine a category associated with the item based on the similarity value satisfying the threshold. The second vector may be associated with the category. The device may provide information that identifies the category associated with the item to cause an action to be performed.

Abstract: A device may communicate with a server to obtain historical ticket data. The device may generate a data model, based on the historical ticket data. The device may communicate with a client device to obtain ticket data relating to an issue associated with a project. The device may classify, using the data model, the ticket data into a ticket type. The device may generate, using the data model and based on the ticket type, a set of recommended resolutions for resolving the issue associated with the project. The device may select, from the set of recommended resolutions, a particular resolution based on a set of selection criteria. The device may automatically perform one or more actions to implement the particular resolution to resolve the issue associated with the project.

Abstract: Privacy is defined in the context of a guessing game based on the so-called guessing inequality. The privacy of a sanitized record, i.e., guessing anonymity, is defined by the number of guesses an attacker needs to correctly guess an original record used to generate a sanitized record. Using this definition, optimization problems are formulated that optimize a second anonymization parameter (privacy or data distortion) given constraints on a first anonymization parameter (data distortion or privacy, respectively). Optimization is performed across a spectrum of possible values for at least one noise parameter within a noise model. Noise is then generated based on the noise parameter value(s) and applied to the data, which may comprise real and/or categorical data. Prior to anonymization, the data may have identifiers suppressed, whereas outlier data values in the noise perturbed data may be likewise modified to further ensure privacy.

Abstract: Summarizing, the application relates to a machine-learning system for adaptively changing a matching threshold of a biometric system. The machine-learning system comprises a batch aggregator device operable to receive input data from the biometric system via a communication interface and to aggregate a batch of at least some of the received input data. The machine-learning system further comprises a learning expert device operable to compute a new suggestion for a matching threshold value of the biometric system based on the aggregated batch. Finally, the machine-learning system comprises an output device operable to output the computed new suggestion for the matching threshold of the biometric system via the communication interface.

Abstract: A device may identify a ticket associated with an issue, a user associated with resolving the issue, and a particular business process including a particular business process step associated with the issue. The device may present information regarding a particular business process workflow, corresponding to the particular business process, associated with a business process steps. The device may identify the particular business process step from the business process steps, and may identify a particular technical solution corresponding to the particular business process step. The device may present information regarding a particular technical solution workflow, corresponding to the particular technical solution, associated with technical solution steps. The device may identify a particular technical solution step, from the technical solution steps, associated with resolving the issue.

Abstract: An unsupervised machine learning model can make prediction on time series data. Variance of time-varying parameters for independent variables of the model may be restricted for continuous consecutive time intervals to minimize overfitting. The model may be used in a control system to control other devices or systems. If predictions for the control system are for a higher granularity time interval than the current mode, the time-varying parameters of the model are modified for the higher granularity time interval.

Abstract: A device may receive input data regarding a particular field of analysis. The device may determine a mapping of a set of metrics of the input data to a set of coordinates. The device may generate a representation of the set of metrics based on the mapping of the set of metrics. A group of pixels may be caused to provide a particular visualization corresponding to a value of a particular metric mapped to a particular coordinate based on the mapping of the set of metrics. The device may provide a plurality of versions of the representation. The particular metric may map to the particular coordinate in each of the plurality of versions of the representation. The plurality of versions of the representation being associated with one or more changes to a particular parameter of the input data.

Abstract: An electronic certificate system facilitates the secure application of certificates against wireless data transfers with a distributer system by a device user. A certificate enabled device carried by the user stores electronic certificates in memory. When the distributer system detects a certificate enabled device within a proximity radio frequency field, the distributer system queries and obtains applicable certificates for the user from the certificate enabled device carried by the user, applies the certificates to a data transfer, and securely communicates the resulting data transfer metadata back to the certificate enabled device.

Abstract: In an example, an ontology analyzer may generate an ontology, based on a claim adjudication request. The claim adjudication request may be processed, based on the ontology to provide an ontology based inference. A rule based analyzer may identify a predefined rule corresponding to the claim adjudication request and process the request, based on the predefined rule. A conflict resolver may resolve a conflict which may occur between the ontology based inference and the rule based inference. When a conflict is detected, a predefined criteria may be selected for resolving the conflict, the predefined criteria comprising rules to select one of the ontology based inference and the rule based inference to maximize a probability of accurately processing the claim adjudication request in case of a conflict.