Abstract: A method extracts explainable corpora embeddings. The method includes constructing a graph with nodes representing terms from a text sequence and edges that include pointwise values generated between pairs of terms. The method further includes generating a rank vector from the graph. Elements of the rank vector correspond to the edges of the graph. A rank value, of the rank values, corresponds to a term from the text sequence. The method further includes selecting the term by comparing the rank vector to a previous rank vector generated for a previous text sequence.

Abstract: A computer-implemented method includes receiving data comprising a plurality of application programming interface (API) requests from a plurality of client devices. The method includes generating a plurality of API sessions based on the data, wherein each of the API sessions is associated with a corresponding client device of the plurality of client devices and includes a sequence of API requests originating from the corresponding client device. The method includes comparing each of the plurality of API sessions to one or more of a plurality of different patterns indicative of permissible API sessions determined based on training data. The method includes determining one or more API sessions of the plurality of API sessions generated based on the data are abnormal based, at least in part, on the comparing. Finally, the method includes performing one or more actions based on determining the one or more API sessions are abnormal.

Abstract: Systems and methods for controlling traffic flow for software experimentation are disclosed. An example method includes receiving a first traffic from a first plurality of users to receive an existing feature or a new feature, determining, from the first traffic, a first portion of the first plurality of users to receive the existing feature (with the first portion bounded by a first lower threshold and/or a first upper threshold) and a second portion of the first plurality of users to receive a new feature (with the second portion bounded by a second lower threshold and/or a second upper threshold). The method also includes providing the existing feature to the first portion, providing the new feature to the second portion, receiving feedback from the first plurality of users, and adjusting the first portion and/or the second portion based on the feedback and based on the bounds.

Abstract: This disclosure relates to restricting access in a social network. The social network stores profile information for each of a plurality of users of the social network in a database. The social network receives, from a first user of the social network, a request to invite a second user to establish a connection with the first user. The social network transmits, to the first user, one or more questions pertaining to the profile information of the second user. The social network receives, from the first user, one or more answers responsive to the one or more questions. The social network determines whether each of the answers is correct based on the stored profile information of the second user. The social network transmits, to the second user, an invitation to establish the connection with the first user when at least a number of the answers are correct.

Abstract: A method implements brand engine for extracting and presenting brand data with user interfaces. The method includes receiving a blueprint with a set of structure blocks extracted from a selected content. A structure block of the set of structure blocks includes a set of style parameter requests for a section of the selected content. The method further includes processing the set of structure blocks with a first set of smart blocks to generate a set of scores. A smart block of the first set of smart blocks includes brand data with style parameter selections. The method further includes selecting a second set of smart blocks, for the set of structure blocks, from the first set of smart blocks, using the set of scores. The method further includes presenting the second set of smart blocks with the brand data.

Abstract: Certain aspects of the present disclosure provide techniques for executing a function in a software application through a conversational user interface based on a knowledge graph associated with the function. An example method generally includes receiving a request to execute a function in a software application through a conversational user interface. A graph definition of the function is retrieved from a knowledge engine. Input is iteratively requested through the conversational user interface for each parameter of the parameters identified in the graph definition of the function based on a traversal of the graph definition of the function. Based on a completeness graph associated with the function, it is determined that the requested inputs corresponding to the parameters identified in the graph definition of the function have been provided through the conversational user interface. The function is executed using the requested inputs as parameters for executing the function.

Abstract: A method scalably authorizes requests. A request to authorize access to a resource is received. A plurality of policies controlling the request is identified. The plurality of policies are concurrently processed. A decision for a policy is received. The decision is of a plurality of decisions corresponding to the plurality of policies. The policy is of the plurality of policies. The decision is determined using a machine learning model and the request. An aggregate decision is generated from the plurality of decisions. A token to access the resource is transmitted in response to the aggregate decision.

Abstract: Aspects of the present disclosure provide techniques for improved text classification. Embodiments include providing, based on a text string, one or more first inputs to a summary model. Embodiments include determining, based on one or more first outputs from the summary model in response to the one or more first inputs, a summarized version of the text string. In some embodiments the summarized version of the text string comprises a number of tokens that is less than or equal to a maximum number of input tokens for a machine learning model. Embodiments include providing, based on the summarized version of the text string, one or more second inputs to the machine learning model. Embodiments include determining one or more attributes of the text string based on one or more second outputs received from the machine learning model in response to the one or more second inputs.

Abstract: Certain aspects of the disclosure provide a method for transferring an achievement token, comprising: receiving a request to transfer an achievement token to a user; querying a smart contract to obtain a requirement associated with the achievement token; verifying, via a blockchain, the user completed the requirement, including retrieving user evidence associated with the requirement from the blockchain; and storing user evidence with a transaction history associated with the transfer of the achievement token to the user; and transferring, via the blockchain, the achievement token to the user.

Abstract: A method optimizes questions to retain engagement. The method includes generating, using a machine learning model, a churn risk from user interaction data. The method includes selecting, when the churn risk satisfies a threshold, a field, from multiple fields, using multiple prediction confidences corresponding to multiple prediction values generated for the multiple fields. The method includes obtaining a prediction value for the field and obtaining a question, corresponding to the field, using the prediction value. The method includes presenting the question and receiving a user input in response to the question.

Abstract: A processor may receive an image and identify a plurality of characters in the image using a machine learning (ML) model. The processor may generate at least one word-level bounding box indicating one or more words including at least a subset of the plurality of characters and/or may generate at least one field-level bounding box indicating at least one field including at least a subset of the one or more words. The processor may overlay the at least one word-level bounding box and the at least one field-level bounding box on the image to form a masked image including a plurality of optically-recognized characters and one or more predicted fields for at least a subset of the plurality of optically-recognized characters.

Abstract: Certain aspects of the present disclosure provide techniques for determining similarities between businesses. One example method generally includes receiving a similarity query and receiving transaction data associated with a plurality of businesses for comparing the plurality of businesses. The method further includes generating a set of vectors representing the plurality of businesses based on the transaction data and generating a set of compact vectors based on the vectors by applying a dimensionality reduction technique. The method further includes generating based on the set of compact vectors, a similarity index and determining a response to the similarity query using the similarity index.

Abstract: A method may including obtaining a voice transcript corpus and a chat transcript corpus, extracting voice transcript sentences from the voice transcript corpus and chat transcript sentences from the chat transcript corpus, encoding, by a series of neural network layers, the voice transcript sentences to generate voice sentence vectors, encoding, by the series of neural network layers, the chat transcript sentences to generate chat sentence vectors, determining, for each voice sentence vector, a matching chat sentence vector to obtain matching voice-chat vector pairs, and adding, to a parallel corpus, matching voice-chat sentence pairs using the matching voice-chat vector pairs. Each of the matching voice-chat sentence pairs may include a voice transcript sentence and a matching chat transcript sentence. The method may further include training a disfluency remover model using the parallel corpus.

Abstract: A method includes processing a set of query texts to identify a set of expressions, where each expression references a set of columns of datetime data in a datastore. The method also includes training a statistical model to determine a distribution of the datetime data for each column that was identified. The method further includes processing the set of expressions to generate a directed graph including more than one nodes and a plurality of edges, where each node represents one of the columns or a transformation applied by one of the expressions to one of the columns. The method additionally includes generating a weight for edges of the directed graph according to a distribution of the datetime data in the columns and a usage index of a corresponding expression.

Abstract: Certain aspects of the present disclosure provide techniques for interacting with a graph database structure. In one embodiment, a method includes receiving, at an application, information regarding a first entity; transmitting, to a graph database, a query regarding the first entity; receiving, at the application, query results based on one or more relationships between the first entity and other entities in the graph database; making, by the application, an inference based on the query results; modifying, by the application, a user interface of the application based on the inference by displaying at least one user interface element suggesting a selection of an application option; and receiving, by the application, a user selection of the suggested application option.

Abstract: Certain aspects of the present disclosure provide techniques for optimizing results generated by functions executed using a rule-based knowledge graph. The method generally includes generating a neural network based on a knowledge graph and inputs for performing a function using the knowledge graph. Inputs for the function are received and used to generate a result of the function. A request to optimize the generated result of the function is received. A loss function is generated for the neural network. Generally, the loss function identifies a desired optimization for the function. Values of parameters in the neural network are adjusted to optimize the generated result based on the generated loss function, and the adjusted values of the parameters in the neural network are output in response to the request to optimize the generated result of the function.

Abstract: A method implements influencer segmentation detection. The method includes selecting transaction data for a time window and processing the transaction data for the time window to generate a graph for the time window. The method further includes extracting, from the graph, a feature set for a node of the graph for the time window and processing the feature set to generate a predicted rank for the node for a subsequent time window using a machine learning model. The method further includes selecting, using the predicted rank, an entity identifier corresponding to the node and presenting the entity identifier.

Abstract: A method of score prediction uses hierarchical attention. Word features, positioning features, participant embedding features, and metadata are extracted from a transcript of a conversation. A word encoder vector is formed by multiplying weights of a word encoder layer to one or more word features. A sentence vector is formed by multiplying weights of a word attention layer to word encoder vectors. An utterance encoder vector is formed by multiplying weights of an utterance encoder layer to the sentence vector. A conversation vector is formed by multiplying weights of an utterance attention layer to utterance encoder vectors. The utterance encoder vector is combined with one or more positioning features and one or more participant embedding features. A predicted net promoter score is generated by multiplying weights of an output layer to the conversation vector combined with the metadata. The predicted net promoter score is presented in a list of conversations.

Abstract: Systems and methods for mapping an electronic document to a particular transaction category are disclosed. An example method may be performed by one or more processors of a categorization system and include receiving, from a user via an interface, an electronic document associated with a transaction between the user and a particular establishment, receiving, from the user via the interface, an image of the particular establishment, identifying in the image, using an image recognition engine, at least one of a sign or a symbol representative of the particular establishment, extracting, using an analytics module, location information from at least one of the image or a mobile device, determining, using the analytics module, a name of the particular establishment based on at least one of the location information or the at least one sign or symbol, and mapping the electronic document to a particular transaction category based on the determined name.