Abstract: A device may receive a request to identify items that satisfy parameters of the request. The device may identify a plurality of items that satisfy the parameters. The device may generate a plurality of explanation sets. An explanation set of the plurality of explanation sets may relate to an item of the plurality of items. The explanation set may include at least one of: a positive explanation indicating that the item is positively associated with a first characteristic that relates to a first preference of a user, or a negative explanation indicating that the item is negatively associated with a second characteristic that relates to a second preference of the user. The device may select an item from the plurality of items based on the plurality of explanation sets. The device may provide information that includes an explanation set of the item selected.

Abstract: A device determines, from a network slice template associated with a network slice, a quality of service (QoS) profile for the network slice that includes performance metrics for corresponding QoS parameters associated with providing a service. The device monitors performance of the network slice in association with the QoS profile, and determines, based on the performance, that a performance indicator for a QoS parameter of the network slice is outside a threshold range of a performance metric. The device determines, based on the performance information and the QoS profile, a slice modification to the network slice template for the network slice, where the slice modification is configured to cause the performance indicator to be within the threshold range of the performance metric. The device causes a network slice orchestrator to update an instantiation of the network slice according to the slice modification and the network slice template.

Abstract: A device receives a request associated with standardizing organization-specific roles within an organization, where the request includes data that identifies titles for the organization-specific roles. The device converts the data to vectors that represent semantic meanings of the titles. The device sets a configuration of a data model by assigning weighted values to title-class identifiers that are used to associate titles, of a standardized set of titles, to a hierarchy of role classifications. The device uses the data model to determine scores that indicate likelihoods of the titles mapping to the title-class identifiers. The device identifies, based on scores, a subset of title-class identifiers that associate particular titles, of the standardized set of titles, and particular role classifications. The subset of title-class identifiers is stored in association with information relating to the particular titles. The device performs an action based on the information relating to the particular titles.

Abstract: A device obtains prescription information relating to a medication in a container. The device causes a camera device of the device to obtain image data relating to the medication and a weighing device of the device to obtain weight data relating the medication. The device sends the prescription information, the image data, and the weight data to a different device to cause the different device to verify the medication using a machine learning model. The device receives information concerning the medication and automatically generates, based on the information concerning the medication, a message concerning the medication, wherein the message includes instructions on how much of the medication a user of the device is to take. The device causes the device or an additional device to present the message.

Abstract: A method and system for generating automated front-end code for a website from design files is described. In one embodiment, a method for generating automated front-end code for a website includes obtaining at least one design file associated with a design of a website from a client device. Hypertext markup language (HTML) code and a cascading style sheet (CSS) file is automatically generated from the at least one design file from information obtained from a plurality of layers associated with the design file. The method includes extracting a plurality of extracted image files from the at least one design file. The method further includes providing front-end code for the website that includes the HTML code, the CSS file, and the plurality of extracted image files to the client device.

Abstract: A system for processing a digital transaction includes a user authenticator, a processor in communication with the user authenticator, and non-transitory computer readable media that stores instruction code. The user authenticator is configured to receive biometric data associated with a user from a sensor. The sensor corresponds to at least one of: an image sensor, a finger print sensor, a motion sensor, a user interaction pattern sensor, and an infrared image sensor. The user authenticator is further configured to receive user identifying information and to determine whether the biometric data associated with the user is associated with the user identifying information to thereby validate the user. If the user is validated, the user authenticator is configured to generate user credentials associated with the user. The instruction code is executed by the processor and causes the processor to receive, from a user terminal, the user credentials.

Abstract: Methods, systems, and apparatus for solving cost optimization problems. In one aspect, a method includes receiving data representing a cost optimization problem in a network, wherein i) the network is represented as a graph of nodes and edges, and ii) each edge comprises an associated cost; mapping the data representing the cost optimization problem in a network to a quadratic unconstrained binary optimization (QUBO) formulation of the cost optimization problem, the QUBO formulation comprising multiple variables with values determined by states of respective qubits, wherein each qubit corresponds to a respective edge of the graph of nodes and edges; obtaining data representing a solution to the cost optimization problem from a quantum computing resource; and initiating an action based on the obtained data representing a solution to the cost optimization problem.

Abstract: The proposed systems and methods describe an autonomous asset detection system that leverages artificial intelligence (AI) models for three-dimensional asset identification and damage detection, asset damage classification, automatic in-field asset tag readings, and real-time asset management. In some embodiments, a deep learning-based system receives a set of aerial images of one or more assets and automatically identifies each asset in the image(s) using rotational coordinates. In some embodiments, an image annotation tool labels the images either manually or automatically. The system then detects whether the asset is damaged and, if so, determine the type of damage, and further captures and stores asset tag information for the target asset. The collected and processed data is then provided to end-users via a comprehensive user interface platform for managing the assets in real-time.

Abstract: A device may receive content data identifying content created by users and metadata associated with the content. The device may receive rules data identifying rules associated with utilization of the content. The device may utilize the metadata to generate digital DNA signatures for the content in near-real time. The device may store, in a repository, the rules data, the content, the digital DNA signatures, and relationships between the digital DNA signatures. The device may receive, from a client device, new content that is generated based on particular content of the content data and new metadata associated with the new content. The device may utilize the new metadata to generate a new digital DNA signature for the new content. The device may process the new digital DNA signature, the rules data, and the digital DNA signatures to determine whether the new content violates one or more rules of the rules data.

Abstract: Systems and methods may reduce bias in an artificial intelligence model. The system may receive word embedding model generated based on a corpus of words. The system may determine a bias definition vector in an embedding space of the word embedding model. The system may receive bias classification criteria. The bias classification criteria may include logic to group word vectors in the word embedding model based on a distance measurement from the bias definition vector. The system may identify, in the word embedding model, a first group of vectors and a second group of vectors based on the bias classification criteria and the bias definition vector. The system may generate a debiased artificial intelligence model. The debiased artificial intelligence model may include associations between words and metrics. The system may weight the metrics for the words associated with the first and second group of vectors with a non-zero penalization factor.

Abstract: A system and method for improving outcomes in various person-to-person goal-oriented interactions is disclosed. Specifically, the method and system enable intelligent insights and recommendations to be presented to a facilitator of the interaction via a virtual assistant user interface. When the facilitator seeks to produce a specific goal with respect to the participant, the system can be configured to automatically generate, based on a series of inputs about the participant and previous interactions, intelligent recommendations that have a high likelihood of successfully promoting the target goal in the current scenario.

Abstract: A system may receive captured content identifying a first face of a user of a user device and media content identifying a second face of a person. The system may process the captured content and the media content, using a first machine learning model, to determine embeddings for the captured content and to identify landmarks in the media content. The system may combine the captured content with the media content, based on the landmarks and the embeddings, to generate combined content and to segment a combined face of the combined content, to replace the second face of the media content with the combined face, and to generate new media content that includes the combined face. The system may process the new media content, with a second machine learning model, to blend the combined face in the new media content and to generate final media content.

Abstract: Methods and systems for dynamic dashboard administration are presented. Embodiments include displaying a dashboard through a graphical user interface (‘GUI’); receiving user instructions to manipulate one or more elements of the dynamic dashboard; capturing the manipulation of the one or more GUI elements; parsing, according to a gesture taxonomy and gesture ontology, the captured manipulation into one or more gesture triples; storing the gesture triples in an enterprise knowledge graph of a semantic graph database; identifying, in dependence upon the gesture triples, dashboard insights; updating the dashboard in accordance with the dashboard insights.

Abstract: A device may obtain first data relating to a machine learning model. The device may pre-process the first data to alter the first data to generate second data. The device may process the second data to select a set of features from the second data. The device may analyze the set of features to evaluate a plurality of types of machine learning models with respect to the set of features. The device may select a particular type of machine learning model for the set of features based on analyzing the set of features to evaluate the plurality of types of machine learning models. The device may tune a set of parameters of the particular type of machine learning model to train the machine learning model. The device may receive third data for prediction. The device may provide a prediction using the particular type of machine learning model.

Abstract: In some implementations, a device may receive user request data from a user device associated with a user. The device may retrieve pre-defined technical object content data from an enterprise system. The device may retrieve pre-defined interface format data and a pre-defined business rule from a first data structure and a second data structure, respectively, based on the user request data. The device may determine whether a mapping, for generating an enterprise system object, is stored in a third data structure. The device may map the technical object content data to the pre-defined interface format data, based on the mapping and the pre-defined business rule, to generate mapped data when the mapping is stored in a third data structure. The device may generate the enterprise system object based on the mapped data. The device may perform one or more actions based on the enterprise system object.

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: Knowledge graph systems are disclosed for enhancing a knowledge graph by generating a new node. The knowledge graph system converts a knowledge graph into an embedding space, and selects a region of interest from within the embedding space. The knowledge graph system further identifies, from the region of interest, one or more gap regions, and calculates a center for each gap region. A node is generated for each gap region, and the information represented by the node is added to the original knowledge graph to generate an updated knowledge graph.

Abstract: This disclosure is directed to a computer system and method to assist in identifying data feature intersection or overlap between private datasets without revealing any specific data items or data features in the datasets. Various technical components including natural language processing, lexical optimization, and encryption and key management technologies such as homomorphic encryption and secret sharing and coding, are integrated into the disclosed system and method to achieve the data feature intersection identification. Such a system and method may be employed in circumstances where data feature intersection is important for collaborative efforts between entities.

Abstract: Aspects of the present disclosure provide an environmental probe configured to be at least partially inserted into the ground at a location and to provide defensible space monitoring and maintenance for a home or other structure. The environmental probe may include one or more environmental sensors configured to perform various environmental measurements to generate environmental measurement data. The environmental probe (e.g., a processor of the environmental probe) may compare the environmental measurement data to one or more thresholds to determine an alert state associated with the location. The environmental probe may include one or more wireless interfaces configured to enable communication with a remote device, such as a smart hub device or other environmental probes. The environmental probe may transmit an indicator of the alert state to the remote device to enable performance of one or more operations based on the alert state.

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 accessing rules that each relate one or more values of the feature vectors to a respective label of a plurality of labels. The actions further include, based on the rules, generating heuristics that each identify related values of the feature vectors. The actions further include, for each of the heuristics, generating a matrix that reflects a similarity of the feature vectors. The actions further include, based on the matrices that each reflects a respective similarity of the feature vectors, generating clusters that each include a subset of the feature vectors. The actions further include, for each cluster, determining a label of the plurality of labels.