Abstract: Embodiments provide a method and a system for conducting merchant-industry mapping based on ambient noise. The method includes receiving a payment transaction request and an audio signal comprising ambient noise in surrounding of a merchant terminal of a merchant. The ambient noise is recorded by the merchant terminal and includes a plurality of sounds. The method includes determining whether the merchant is an aggregated merchant or a non-aggregated merchant. The method includes processing the ambient noise to determine a type of the merchant upon determining that the merchant is the non-aggregated merchant. The method includes mapping the merchant to an industry from a plurality of industries available in the server system based on the type of the merchant. The method further includes storing the mapping of the industry and the merchant in a database associated with the server system.

Abstract: Implementations of the subject technology provide systems and methods for multi-mode voice triggering for audio devices. An audio device may store multiple voice recognition models, each trained to detect a single corresponding trigger phrase. So that the audio device can detect a specific one of the multiple trigger phrases without consuming the processing and/or power resources to run a voice recognition model that can differentiate between different trigger phrases, the audio device pre-loads a selected one of the voice recognition models for an expected trigger phrase into a processor of the audio device. The audio device may select the one of the voice recognition models for the expected trigger phrase based on a type of a companion device that is communicatively coupled to the audio device.

Abstract: One or more computer processors parse a received natural language question into an abstract meaning representation (AMR) graph. The one or more computer processors enrich the AMR graph into an extended AMR graph. The one or more computer processors transform the extended AMR graph into a query graph utilizing a path-based approach, wherein the query graph is a directed edge-labeled graph. The one or more computer processors generate one or more answers to the natural language question through one or more queries created utilizing the query graph.

Abstract: A modular two-stage neural architecture is used in translating a natural language question into a logic form such as a SPARQL Protocol and RDF Query Language (SPARQL) query. In a first stage, a neural machine translation (NMT)-based sequence-to-sequence (Seq2Seq) model translates a question into a sketch of the desired SPARQL query called a SPARQL silhouette. In a second stage a neural graph search module predicts the correct relations in the underlying knowledge graph.

Abstract: An embodiment includes decomposing a natural language assertion into a natural language question and answer pair that includes an initial question and an initial answer. The embodiment translates the initial question into a structured knowledge graph query and then performs an iterative process comprising iterative querying of a knowledge graph and evaluating of corresponding query responses resulting in respective confidence scores. A first iteration of the iterative process comprises querying of the knowledge graph to retrieve a first predicted answer, then determining whether a degree of similarity between the initial answer and the first predicted answer meets a threshold criterion. If not, the first predicted query is altered and used for querying the knowledge graph in a subsequent iteration of the iterative process. The embodiment also generates an assertion correctness score indicative of a degree of confidence that the assertion is factual using the respective confidence scores.

Abstract: According to one embodiment of the present invention, a system transforms an application for a distributed computing environment. The system comprises one or more memories, and at least one processor coupled to the one or more memories. The system analyzes a description of user intent to extract information for transforming the application. The extracted information indicates functionalities for the distributed computing environment. A plurality of software artifacts of the application are mapped to the functionalities. The plurality of software artifacts form different groups of software artifacts. Remaining software artifacts of the application are assigned into the different groups based on a remaining software artifact corresponding to a mapped software artifact of a group. The different groups correspond to microservices for the distributed computing environment. The microservices for the distributed computing environment are presented based on the different groups.

Abstract: Implementations of the subject technology provide systems and methods for multi-mode voice triggering for audio devices. An audio device may store multiple voice recognition models, each trained to detect a single corresponding trigger phrase. So that the audio device can detect a specific one of the multiple trigger phrases without consuming the processing and/or power resources to run a voice recognition model that can differentiate between different trigger phrases, the audio device pre-loads a selected one of the voice recognition models for an expected trigger phrase into a processor of the audio device. The audio device may select the one of the voice recognition models for the expected trigger phrase based on a type of a companion device that is communicatively coupled to the audio device.

Abstract: Methods, systems, and computer program products for zero-shot entity linking based on symbolic information are provided herein. A computer-implemented method includes obtaining a knowledge graph comprising a set of entities and a training dataset comprising text samples for at least a subset of the entities in the knowledge graph; training a machine learning model to map an entity mention substring of a given sample of text to one corresponding entity in the set of entities, wherein the machine learning model is trained using a multi-task machine learning framework using symbolic information extracted from the knowledge graph; and mapping an entity mention substring of a new sample of text to one of the entities in the set using the trained machine learning model.

Abstract: Implementations of the subject technology provide systems and methods for multi-mode voice triggering for audio devices. An audio device may store multiple voice recognition models, each trained to detect a single corresponding trigger phrase. So that the audio device can detect a specific one of the multiple trigger phrases without consuming the processing and/or power resources to run a voice recognition model that can differentiate between different trigger phrases, the audio device pre-loads a selected one of the voice recognition models for an expected trigger phrase into a processor of the audio device. The audio device may select the one of the voice recognition models for the expected trigger phrase based on a type of a companion device that is communicatively coupled to the audio device.

Abstract: A method for predicting the behavior of an electronic social network (ESN) includes identifying one user's connections with other users and creating a data structure in a memory that represents the users and their connections in the ESN. A plurality of data sources for electronic communications between users are analyzed and assigned a relative importance value. A weight is also assigned to each of the connections between the users. The weight is an encoded value computed based on a link structure of the connections where the link structure includes metadata indicating a category and a status of the respective connection. The probability that one user will communicate with one of the other users is calculated based on the analyzed plurality of data sources calculating, and the user's connections with respect to other users are ranked based on the calculated probabilities.

Abstract: One or more computer processors parse a received natural language question into an abstract meaning representation (AMR) graph. The one or more computer processors enrich the AMR graph into an extended AMR graph. The one or more computer processors transform the extended AMR graph into a query graph utilizing a path-based approach, wherein the query graph is a directed edge-labeled graph. The one or more computer processors generate one or more answers to the natural language question through one or more queries created utilizing the query graph.

Abstract: Methods, systems, and computer program products for explaining cross domain model predictions are provided herein. A computer-implemented method includes providing a test data point to a domain adaptation model to obtain a prediction, wherein the domain adaptation model is trained on a set of labeled data points and a set of unlabeled data points. The method includes generating a task specific explanation for the prediction that includes one or more data points from among the sets that satisfy a threshold score for influencing the prediction. Additionally, the method includes generating a domain invariant explanation for the prediction. The domain variation explanation is generated by ranking pairs of data points based on a statistical similarity to the test data point, wherein each pair includes a data point from the set of labeled data points and a data point from the set of unlabeled data points.

Abstract: Implementations of the subject technology provide systems and methods for multi-mode voice triggering for audio devices. An audio device may store multiple voice recognition models, each trained to detect a single corresponding trigger phrase. So that the audio device can detect a specific one of the multiple trigger phrases without consuming the processing and/or power resources to run a voice recognition model that can differentiate between different trigger phrases, the audio device pre-loads a selected one of the voice recognition models for an expected trigger phrase into a processor of the audio device. The audio device may select the one of the voice recognition models for the expected trigger phrase based on a type of a companion device that is communicatively coupled to the audio device.

Abstract: A method, computer system, and a computer program product for computing a product drag effect is provided. The present invention may include receiving a plurality of transaction record data. The present invention may then include tuning a plurality of parameters based on the received transaction record data. The present invention may further include determining a product drag frequency based on the authorized parameter tuning and received transaction record data. The present invention may then include calculating a drag probability based on the determined product drag frequency. The present invention may then include deriving an observation from the calculated drag probability. The present invention may lastly include outputting the derived observation to a user.

Abstract: A method for predicting the behavior of an electronic social network (ESN) includes identifying one user's connections with other users and creating a data structure in a memory that represents the users and their connections in the ESN. A plurality of data sources for electronic communications between users are analyzed and assigned a relative importance value. A weight is also assigned to each of the connections between the users. The weight is an encoded value computed based on a link structure of the connections where the link structure includes metadata indicating a category and a status of the respective connection. The probability that one user will communicate with one of the other users is calculated based on the analyzed plurality of data sources calculating, and the user's connections with respect to other users are ranked based on the calculated probabilities.

Abstract: Methods, systems, and computer program products for explaining cross domain model predictions are provided herein. A computer-implemented method includes providing a test data point to a domain adaptation model to obtain a prediction, wherein the domain adaptation model is trained on a set of labeled data points and a set of unlabeled data points. The method includes generating a task specific explanation for the prediction that includes one or more data points from among the sets that satisfy a threshold score for influencing the prediction. Additionally, the method includes generating a domain invariant explanation for the prediction. The domain variation explanation is generated by ranking pairs of data points based on a statistical similarity to the test data point, wherein each pair includes a data point from the set of labeled data points and a data point from the set of unlabeled data points.

Abstract: A method for predicting the behavior of an electronic social network (ESN) includes identifying one user's connections with other users and creating a data structure in a memory that represents the users and their connections in the ESN. A plurality of data sources for electronic communications between users are analyzed and assigned a relative importance value. A weight is also assigned to each of the connections between the users. The weight is an encoded value computed based on a link structure of the connections where the link structure includes metadata indicating a category and a status of the respective connection. The probability that one user will communicate with one of the other users is calculated based on the analyzed plurality of data sources calculating, and the user's connections with respect to other users are ranked based on the calculated probabilities.

Abstract: Systems and computer program products may rank one user's connections in an electronic social network. The computer system may include a processor and a memory. The computer system may additionally include a program comprising a plurality of instructions stored in memory that are executed by the processor to identify one user's connections with other users in an electronic social network, and analyze a plurality of data sources for electronic communications between the one user and the other users. The program may additionally include a plurality of instructions stored in memory that are executed by the processor to calculate for each of the other users the probability that the one user will communicate with that other user based on the analyzed plurality of data sources, and rank the one user's connections with the other users based on the calculated probabilities.

Abstract: A call center system for allocating problem tickets for technical services by using a pull model auction to select an agent to work on the problem ticket. When the call center receives an order for a technical service, it develops a complexity estimate for the tasks specified in the problem ticket, and calculates deadlines for completing the problem ticket and for conducting the auction. Invitations to bid on the problem ticket are sent to potential agents on a bidder's list. The winning bid is selected from among the bids received back from the potential agents, and, after evaluating the bids, the problem ticket is transferred to the winning agent.

Abstract: One embodiment provides a method, including: obtaining information corresponding to a plurality of transactions of an entity, each transaction comprising at least one bundle of items, wherein the information identifies (i) the items included in each bundle and (ii) the cost of each of the bundles to a customer who purchased the bundle; identifying attributes of the items included in the transactions; determining a cost individually attributable to the items included in the transactions; generating at least one bundle of items using the identified attributes of individual items, each of the generated bundles having (i) an identified cost of the bundle to a customer derived from the determined cost of individual items and (ii) an identified perceived value of the bundle to the customer; and providing a recommendation to the entity regarding items to bundle based upon the generated at least one bundle of items.