Abstract: A method may include receiving first and second application programming interface (API) specifications. The method may include comparing first and second API Specification according to a set of criteria. Responsive to the first and second criteria being identical, the method may include identifying first and second objects. Responsive to the first and second criteria being identical, the method may include classifying a type of the first and second objects and associating one or more parameters in the first and second objects with a consolidated object. The one or more parameters in the first and second objects may be associated with the consolidated object based on the type of the first and second object. The method may include generating a consolidated specification that includes the consolidated object and a description of the one or more parameters of the first and second objects that are associated with the consolidated object in a single API specification.

Abstract: A system may include a memory, a communication interface, and a processor operatively coupled to the communication interface. The processor may be configured to generate a set of classification models. The classification models may include one or more sentences associated with application program interface (API) documentations. The processor may also be configured to access one or more content sources for content. Additionally, the processor may be configured to determine whether one or more sentences in the content is the same or substantially similar to one or more sentences included in the classification models. In response to determining one or more sentences in the content is the same or substantially similar, the processor may be configured to collect the content as API documentation. Additionally, the processor may be configured to categorize different type of API documentations, such as REST API documentations, JAVA API documentations, etc.

Abstract: A system configured for curtailment event implementation based on uncertainty of energy demand flexibility of sites includes a processor and a non-transitory computer-readable medium communicatively coupled to the processor and having encoded therein programming code executable by the processor to perform operations. The operations may include receiving energy usage data from meters at sites and uncertainties of energy demand flexibility for each site. The uncertainties of energy demand flexibility of each site may be zero, may fit a single probability distribution, may fit a set of probability distributions, or may belong to a set of demand flexibilities. The operations may include clustering the sites based on the uncertainties. The operations may include solving group-specific formulations for groups. The operations may include identifying sites to include in the DR event using the solved-for group-specific parameters.

Abstract: According to an aspect of an embodiment, a method may include obtaining a first document associated with a particular API and a first Uniform Resource Identifier (URI), a second document associated with the particular API and a second URI, and an API name of the particular API. The method may further include identifying a URI prefix included in the first URI and the second URI. The method may further include determining a correlation between the URI prefix and the API name. The method may further include determining that the URI prefix is associated with the particular API based on the correlation. The method may further include associating the first document and the second document with the particular API based on the first URI and the second URI including the URI prefix and based on determining that the URI prefix is associated with the particular API.

Abstract: A method may include obtaining an application programming interface (API) document, such as an API user guide, API reference, or other API documentation, that includes information regarding an API resource, and extracting information from the API document, where the information including at least one function and at least one table that includes an attribute associated with the API resource. The method may also include extracting a description for each of the at least one functions from the API document, and comparing the extracted at least one table to a model of attributes to correlate extracted attributes with extracted functions. The method may additionally include compiling a machine-readable API specification for the API resource described in the API document based on the correlation between extracted attributes and the extracted functions.

Abstract: A JavaScript Object Notation (JSON) data validation method may include generating a description schema for defining JSON data using one or more JSON constructs. The method may further include converting the description schema to an Extensible Markup Language (XML) schema. The method may also include converting the JSON data to XML data, and validating the JSON data based on the XML Schema and the XML data.

Abstract: A method of machine learning may include receiving an API documentation including an endpoint and corresponding metadata. The method may include receiving a verified API specification including a verified endpoint. The method may include comparing the endpoint to the verified endpoint. The method may include classifying the metadata. The metadata may be classified as a positive item responsive to the endpoint being substantially the same as the verified endpoint. The metadata may be classified as a negative item responsive to the endpoint not being substantially the same as the verified endpoint. The method may include generating a training dataset, which may include the metadata classified as a positive item or a negative item. The method may include generating a metadata model using a machine learning algorithm and the training dataset. The metadata model may be configured to be used to determine whether an unknown endpoint is a positive endpoint.

Abstract: A method of evaluating and selecting an application programming interface (API). The method includes accessing source-code in computer programming languages from an API repository. The method includes training a machine learning classifier using generalized, extracted API-calls to learn variations of API-call programming language syntaxes through usage of the API-call programming language syntaxes in the source-code. The method includes building API-call models suitable for the computer programming languages and building a parameter relations graph based on backtracking data-flow from API-calls that are extracted from public project repositories based on the API-call model. The method includes retrieving sample parameter values from the parameter relations graph. The method includes implementing the sample parameter values in an evaluation interface to evaluate functionality of an API in a software application.

Abstract: A gateway includes a communication interface and a processor. The processor is configured to receive, via the communication interface, a graphical representation of a device to be connected to the gateway, the graphical representation being in an electronic format. The processor is further configured to identify the device using the graphical representation to locate a record for the device in an electronic data storage. The processor is configured to search, connect and interact with a variety of the Internet of Things (IoT) devices or services. The processor is also configured to record and/or monitor all connected devices. The processor is also configured to provide a recommendation for the possible connection via a graphical user interface based on monitored/recorded patterns, which are permitted. The processor is configured to learn the monitored/recorded patterns based machine learning methods and trigger some actions with users' permissions or recommend a set of services to users.

Abstract: A method of test-assisted application programming interface (API) learning includes generating a machine-readable API specification (API spec). The API spec is based on application of machine learning and regular expression processes to an API documentation. The method includes comparing the API spec to other API specifications. Based on the comparison, the method includes identifying a set of similar API specifications. The method includes generating API test inputs based on the set of similar API specifications and the API spec. The method includes calling a native API system using a first API test input of the API test inputs. The method includes receiving a response indicative of whether the first API test input successfully interacts with the native API system. Based on the response, the method includes generating a feedback indicative of an alteration to the API test inputs or to the machine learning or the regular expression processes.

Abstract: A method may include receiving a request to obtain a data file from a nearest remote computing system in a blockchain network and requesting a transaction history of the data file from the blockchain network. The method may also include determining the nearest remote computing system to the requesting computing system based on the transaction history, where the nearest remote computing system stores a copy of the transaction history and the data file and determining the data file stored on the nearest remote computing system is the same as the requested data file by comparing a first hash value associated with the requested data file to a second hash value associated with the data file stored on the nearest remote computing system. The method may also include receiving the requested data file from the nearest remote computing system; and sending the requested data file to the requesting computing system.

Abstract: The present disclosure includes a method of routing packets. The method may include receiving, at a network device, an interest packet that requests content from an ICN network, where the network device is configured to communicate in the ICN network via one or more paths in an address-routing network. In the method, the ICN network may be overlaid on one or more network devices communicating in the address-routing network. The method may additionally include determining, between a first and second alternative path, a routing path of where to route the interest packet based on network costs associated with obtaining the content along each of the first and the second alternative paths through the address-routing network. The method may also include sending the interest packet along the routing path.

Abstract: Technologies are described to control network congestion in packet-based networks. In some examples, a method may include receiving an Interest packet requesting content, and returning the content from a local data store if the content is in the local data store. The method may also include determining whether a previous request has been made for the requested content if the content is not in the local data store, and creating a record of the Interest packet and discarding the Interest packet if a previous request has been made. The method may further include determining whether a local IP routing table includes an entry that matches a destination IP address specified by the Interest packet if a previous request has not been made, and forwarding the Interest packet if the destination IP address is in the local IP routing table.

Abstract: A method of determining application program interface (API) mashups is provided. The method may include identifying an endpoint description for a first API, and determining whether the endpoint description includes at least one of input description data and output description data. Further, the method may include, in response to the endpoint description including the input description data, determining at least one possible API mashup including the first API and a second API based on a comparison of the input description data and an output description of the second API. Moreover, the method may include, in response to the endpoint description including the output description data, determining one or more possible API mashups including the first API and the second API based on a comparison of the output description data and an input description of the second API.

Abstract: A system for providing access to at least one of an application programming interface (API) and an Internet of Thing (IoT) device is provided. A system may include one or more processors configured to establish a connection between at least one user device and the at least one of the API and the IoT device. The one or more processors may further be configured to allocate resources for one or more services associated with the at least one of the API and the IoT device and provide the one or more services associated with the at least one of the API and the IoT device to the at least one user device. The one or more processors may also be configured to monitor at least one operational parameter associated with the one or more services provided to the at least one user device.

Abstract: A method of public information communication from an open public Internet-of-Things (IoT) device may include measuring a physical phenomenon. The method includes determining a location of an IoT device. The method includes broadcasting a network address and the location of the IoT device such that mobile devices within a particular proximity of the IoT device has access to the network address and the location. The method includes receiving an access request for public information at the network address from a mobile device positioned within the particular proximity to the IoT device. The public information includes a raw form of data measured by the physical sensor that is configured to be locally processed on the mobile devices. The method includes directly communicating the public information to the mobile device via a communication network without authentication or configuration between the IoT device and the mobile device.

Abstract: A method may include obtaining metadata associated with first and second data sets that may be stored at remote computing devices. The method may also include constructing a first database based on the metadata, and performing a first processing task on the first data set. The first processing task may include obtaining raw data of the first data set, and importing the raw data of the first data set into a second database that may be configured as a relational database. The first processing task may also include generating relationships in the second database between entries of the first data set to facilitate anticipated responses to queries. The method may additionally include performing a second processing task different from the first processing task on the second data set, and after performing the first processing task, receiving a query that invokes the first data set, and responding to the query.

Abstract: In one or more embodiments, one or more systems, methods, and/or processes may provide first images to a user; determine a rotation of a head of the user; provide second images, based at least on the rotation, to the user; and rotate a platform in accordance with the rotation. In one example, the platform may include a movement belt, and one or more of a walking movement and a running movement of the user may be determine via the movement belt. In another example, one or more optical sensors may be utilized to determine movements of the user. In one or more embodiments, determining the rotation of the head of the user may include utilizing one or more of a gyroscope and an accelerometer of a head mounted display, worn by the user. For example, the head mounted display may provide images of a virtual reality environment to the user.

Abstract: A method of coupon distribution is used in connection with a demand response (DR) event. The method includes clustering DR customers into customer clusters based on energy use behaviors of the DR customers. Suggested coupons are received from merchants, each coupon including load serving entity (LSE) and merchant contributions. Based on energy price forecast, the suggested coupons, and customer information, a coupon distribution is found to maximizes a financial benefit to the LSE and includes an optimal number of the suggested coupons to be distributed to the customer clusters. The suggested coupons are distributed to the customer clusters per the coupon distribution and collecting responses to the suggested coupons indicating participation. Based on the customer responses, the method includes estimating energy curtailment contributions of the DR customers and an actual energy price for the DR event and communicating to an independent system operator an energy transaction bid based thereon.

Abstract: A method of generating application program interface (API) mashups is provided. The method may include grouping a plurality of APIs into a plurality of sub-clusters based on at least one keyword for each API of the plurality of APIs. The method may also include identifying a plurality of keyword combinations for the plurality of sub-clusters based on real-world data and two or more keywords for the plurality of sub-clusters. Further, the method may include determining a plurality of possible API mashups including two or more APIs of the plurality of APIs for the plurality of keyword combinations. The method may also include processing the plurality of possible API mashups to generate at least one prioritized API mashup of the plurality of possible API mashups, the at least one prioritized API mashup associated with at least one keyword combination of the plurality of keyword combinations.