Abstract: A bot network system may be provided. A system may include a plurality of bot agents, wherein at least one bot agent of the plurality of bot agents is configured to receive a request from a user in natural language. The system may further include a plurality of digital resources including one or more of a software program, a service, a web service and a dataset. Each digital resource of the plurality of digital resources may be configured to communicate with a dedicated bot agent of the plurality of bot agents. Also, each bot agent may be configured to interact with its associated digital resource via an application programming interface (API) of the associated digital resource and translate between the natural language and a language of the associated digital resource.

Abstract: Technologies are described to provide control of traffic signals based at least in part on multiple Q-learning categories. In some examples, a method may include clustering historical traffic data into multiple traffic pattern clusters, and generating multiple Q-learning categories, where each Q-learning category corresponds to a traffic pattern cluster of the multiple traffic pattern clusters. The method may also include determining a first Q-learning category of the multiple Q-learning categories to use in controlling traffic signals at an intersection based at least in part on a first traffic data of the intersection, where the first Q-learning category corresponds to a first traffic pattern cluster, and the first traffic data corresponds to the first traffic pattern cluster. The method may additionally include generating a first control action for the traffic signals at the intersection based at least in part on the first Q-learning category.

Abstract: A system may include a communication interface and a processor operatively coupled to the communication interface. The processor may receive a defined API specification, which may include an API table in a first format in which each column may be associated with an API parameter. The processor may generate a key model that includes one or more API parameters of the API table mapped to a corresponding class of API parameters in a defined format based on the defined API specification. The processor may receive an extracted table in which each column may be associated with an API parameter in the first format. The processor may associate each column of the extracted table with a class of API parameters in the defined format based on the key model. The processor may generate an API specification that includes the API parameters of the extracted table in the defined format.

Abstract: A system may include a communication interface and a processor operatively coupled to the communication interface. The processor may receive a defined API specification, which may include an API table in a first format in which each column may be associated with an API parameter. The processor may generate a key model that includes one or more API parameters of the API table mapped to a corresponding class of API parameters in a defined format based on the defined API specification. The processor may receive an extracted table in which each column may be associated with an API parameter in the first format. The processor may associate each column of the extracted table with a class of API parameters in the defined format based on the key model. The processor may generate an API specification that includes the API parameters of the extracted table in the defined format.

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 method may include extracting information regarding a software project based on source code of the software project, where the information includes a description of the source code and computer-readable source code. The method may also include identifying at least two application programming interface (API) calls within the information, and extracting metadata regarding at least one of the APIs from the information. The method may additionally include, using the metadata, analyzing the computer-readable source code to determine a correlation between the two API calls. The method may also include, based on the correlation and using a first of the two API calls, generating computer source code that combines the first API call and an additional API call in an API mashup.

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 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: Technologies are described to provide control of traffic signals based at least in part on multiple Q-learning categories. In some examples, a method may include clustering historical traffic data into multiple traffic pattern clusters, and generating multiple Q-learning categories, where each Q-learning category corresponds to a traffic pattern cluster of the multiple traffic pattern clusters. The method may also include determining a first Q-learning category of the multiple Q-learning categories to use in controlling traffic signals at an intersection based at least in part on a first traffic data of the intersection, where the first Q-learning category corresponds to a first traffic pattern cluster, and the first traffic data corresponds to the first traffic pattern cluster. The method may additionally include generating a first control action for the traffic signals at the intersection based at least in part on the first Q-learning category.

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: 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: Technologies are described to adjust a learning rate of Q-learning being used to control traffic signals at an intersection. In some examples, a method may include generating control actions for traffic signals at an intersection based on Q-learning, determining a frequency of change in traffic pattern of the intersection, and adjusting a learning rate of the Q-learning based on the determined frequency of change in traffic pattern of the intersection. The Q-learning may determine the generated control actions based on at least a portion of historical traffic data of the intersection, and the change in traffic pattern may be a change from a first traffic pattern of the intersection to a second traffic pattern of the intersection.

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 private mutually authenticated key exchange is provided. The method may include receiving, at the first device, a message transmitted from a second device and including a hierarchical inner-product encryption (HIPE) ciphertext. Further, the method may include decrypting, at the first device, the HIPE ciphertext to generate a first authenticated encryption (AE) ciphertext. The method may further include decrypting, at the first device, the first AE ciphertext. Further, the method may include encrypting, at the first device, a second AE ciphertext including a signature and one or more attributes of the first device. Moreover, the method may include transmitting, to the second device, another message including the second AE ciphertext.

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 of the plurality of APIs. The method may also include identifying at least one keyword combination for the one or more sub-clusters based on real-world data and two or more keywords for the plurality of sub-clusters. Further, the method may include determining one or more possible API mashups including two or more APIs of the plurality of APIs for the at least one keyword combination. In addition, the method may include determining a similarity score for each possible API mashup of the one or more possible API mashups. The method may also include identifying at least one API mashups from the one or more possible mashups based on the similarity score for each possible API mashup.

Abstract: A method of critical peak pricing (CPP) demand response (DR) event implementation includes performing an initial CPP participant selection assessment. The method includes generating an initial CPP participant selection and an estimated CPP curtailment based on the initial CPP participant selection assessment. The method includes calculating a reduced day-ahead demand. The method includes evaluating the reduced day-ahead demand. The method includes submitting an energy bid to a market server. The method includes forecasting a revised real time price. The method includes performing a final CPP participation selection assessment. The method may include generating a final CPP participant selection to include the CPP participants having accumulated individual participant revenue effects up to the reduced day-ahead demand. The method includes communicating CPP event notifications to each of the CPP participants in the final CPP participant selection.

Abstract: A method includes determining user interests associated with a user. The method includes receiving multiple offers from multiple merchants that each include a purchase incentive to purchase a good or service and a category of interest associated with the good or service. The method includes identifying a match between the user and an offer from the multiple offers, including matching the user interests associated with the user to the category of interest associated with the good or service. The method includes determining a time period associated with a demand response event. The method includes generating a coupon for the user that includes the offer and a performance bonus that provides a performance incentive to redeem the coupon at a specified physical location during the time period associated with the demand response event. The method includes providing the coupon to the user for redemption at the specified physical location.

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 of the plurality of APIs. The method may also include identifying at least one keyword combination for the one or more sub-clusters based on real-world data and two or more keywords for the plurality of sub-clusters. Further, the method may include determining one or more possible API mashups including two or more APIs of the plurality of APIs for the at least one keyword combination. In addition, the method may include determining a similarity score for each possible API mashup of the one or more possible API mashups. The method may also include identifying at least one API mashups from the one or more possible mashups based on the similarity score for each possible API mashup.

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.

Abstract: A method of demand response (DR) assessment may include reading energy economics data from a third party for an interval of a DR event period. The energy economics data may include a day-ahead demand, a day-ahead locational marginal pricing (LMP), and a real-time LMP. The method may also include estimating a real-time energy demand for the interval. Based on the energy economics data and the estimated real-time energy demand, the method may also include determining a demand imbalance for the interval. The method may also include calculating a cost of a DR event that exploits the demand imbalance. The method may also include selecting an energy curtailment request amount for the interval that optimizes profitability of the DR event.