Abstract: A method may include providing access to a first application programming interface (API) provided by a first party and a second API provided by a second party. The method may also include collecting a first set of API data sources related to the first API and a second set of API data sources related to the second API. The method may additionally include using a deep learning model to predict a privacy trustworthiness level for the first API and the second API, and disabling access to the first API based on the privacy trustworthiness level of the first API being below a threshold level.

Abstract: Operations may include obtaining source code describing an optimization problem. The operations may include identifying problem parameters associated with the optimization problem such that a specialized computing system may be enabled to solve the optimization problem. The operations may include extracting one or more first parameters of the problem parameters from the source code. The operations may include identifying one or more second parameters of the problem parameters that are not included in the source code. A user may be prompted via a GUI for input relating to the one or more second parameters. The operations may include compiling the extracted first parameters and the user-provided second parameters as input parameters of the specialized computing system. The operations may include providing the input parameters to the specialized computing system such that the specialized computing system is able to solve the optimization problem.

Abstract: According to an aspect of an embodiment, operations for trend monitoring of code repositories and related information are provided. The operations include identifying a set of repositories from a collection of repositories hosted on one or more web-based repository hosting systems and collecting repository metadata for each repository. The operations further include generating a set of topic tags by using one or more natural language processing-based methods and collecting a set of statistics associated with each of the generated set of topic tags. The operations further include generating a set of presentation data based on one or more of the identified set of repositories, the collected repository metadata, the generated set of topic tags, and the collected set of statistics. The operations further include controlling a user device to display the generated set of presentation data onto an electronic User Interface of the user device.

Abstract: A method of providing a parent container image may include obtaining container image names, obtaining layer hashes, constructing a structured database, and returning a parent container image. The container image names may be of container images that include static executable software for running a process. The layer hashes may be obtained for each of the container images. The structured database may be based on relationships between the container images, which may be identified using the layer hashes. The parent container image may be returned in response to a query regarding a container image. The parent container image may be identified using the structured database.

Abstract: According to an aspect of an embodiment, operations include deploying a trigger function on a computing system for detecting an electronic trigger event associated with a first Application Programming Interface (API) provider. The operations further include deploying a rule function for applying at least one trigger rule on event data and deploying an action function for generating at least one API call to a second API provider based on whether the event data satisfies the at least one trigger rule. The operations further include deploying a set of API gateways for enabling a workflow system to invoke at least one of: the deployed trigger function, the deployed rule function, and the deployed action function. The operations further include generating a workflow template of an API mashup based on information associated with the deployed functions and API gateways and posting the generated workflow template to the workflow system.

Abstract: A method and system of solving a large-scale complex combinatorial problem including receiving the large-scale complex combinatorial problem as an input, converting a decision variable space of the large-scale complex combinatorial problem into a plurality of basic attribute units which correspond to a subset of total decision variables of the large-scale complex combinatorial problem, decomposing the large-scale complex combinatorial problem into a plurality of sub-problems of the plurality of basic attribute units, using a optimization solver, solving the plurality of sub-problems in parallel, outputting a plurality of candidate solutions corresponding to the solutions of the plurality of sub-problems, and using a optimization solver and the plurality of candidate solutions, solving the large scale complex combinatorial problem.

Abstract: According to an aspect of an embodiment, operations may include receiving a first input corresponding to a selection of a combinatorial optimization problem and receiving a set of datapoints as input. The operations may further include generating a first quadratic unconstrained binary optimization (QUBO) formulation based on an objective function for the combinatorial optimization problem and the received set of datapoints. The operations may further include selecting a first privacy setting and encoding the first QUBO formulation based on the selected privacy setting to generate a second QUBO formulation. The operations may further include submitting the generated second QUBO formulation to an optimization solver machine and receiving a first solution of the second QUBO formulation. The operations may further include decoding the first solution to produce a second solution and publishing an output of the combinatorial optimization problem on a user device based on the second solution.

Abstract: A method comprises receiving vehicle data comprising information associated with a plurality of sensors of autonomous vehicle and segmenting the received vehicle data into non-public data and public data. The method further comprises partitioning the public data into a plurality of data partitions and generating a plurality of data levels of the public data. Each data level of the plurality of data levels is generated according to an access level of a plurality of access levels and includes one or more data partitions of the plurality of data partitions in an encrypted form. The method further comprises transmitting the generated plurality of data levels to a group of electronic devices. Each electronic device of the group of electronic devices retrieves, according to one of the plurality of access levels, at least a portion of the public data from the transmitted plurality of data levels.

Abstract: A method may include receiving software artifacts representing previously developed software entities from one or more repository sources. The method may include constructing a knowledge graph of the software artifacts. The method include training, using the knowledge graph, a graph neural network model to recommend one or more of the previously developed software entities for a software development objective. In some aspects, the method may include generating a recommendation including one or more of the previously developed software entities to be used for the software development objective.

Abstract: Operations may include obtaining source code describing an optimization problem. The operations may include identifying a plurality of problem parameters associated with the optimization problem such that a specialized computing system may be enabled to solve the optimization problem. The operations may include extracting one or more first parameters of the plurality of problem parameters from the source code. The operations may include identifying one or more second parameters of the plurality of problem parameters that are not included in the source code. A user may be prompted via a GUI for input relating to the one or more second parameters. The operations may include compiling the extracted first parameters and the user-provided second parameters as input parameters of the specialized computing system. The operations may include providing the input parameters to the specialized computing system such that the specialized computing system is able to solve the optimization problem.

Abstract: A method for parameter value resolution, includes extracting, from an application programming interface (API) repository, metadata information associated with an API parameter set of at least one API and generating parameter clusters associated with the API parameter set based on the extracted metadata information. Each parameter cluster may include word tokens associated with at least one API parameter of the API parameters set. The method furthers includes determining a representative term for each parameter cluster and based on the determined representative term, extracting sample parameter values from a knowledge base server. The method further includes generating test API endpoints associated with the at least one API based on the extracted sample parameter values, validating the extracted sample parameter values by executing the generated test API endpoints, and publishing, based on the validation, validation result as an indicator for suitability of the at least one API for a software application.

Abstract: According to an aspect of an embodiment, operations include receiving a first input corresponding to a selection of one or more Application Programming Interface (API)-based trigger options associated with one or more electronic trigger events. The operations further include receiving a second input corresponding to a selection of one or more trigger rules which are applicable on event data associated with the one or more electronic trigger events and receiving a third input corresponding to a selection of one or more API-based actions. The operations further include constructing an API mashup template based on the first input, the second input, and the third input and generating an API mashup code based on the constructed API mashup template. The API mashup code is configured to be computer-executable on a runtime system.

Abstract: A method may include obtaining input data for an application programming interface (API), and encrypting the input data for the API using a public key of a provider of the API. The method may also include transmitting, to an API management server, an API request that invokes the API, where the API request includes an API call for the API and the encrypted input data. The API request may be in a format such that the API management server is capable of performing API management services based on the API call but unable to decrypt the encrypted input data with the public key.

Abstract: A method may include obtaining machine-readable source code. The method may include parsing the source code for one or more code descriptions and identifying a section of the source code corresponding to each of the code descriptions. The method may include determining a description-code pair including a first element representing the code description and a second element representing the section of the source code corresponding to the code description. The method may include generating an augmented programming language corpus based on the description-code pair, the one or more code descriptions, and the source code. The method may include receiving a natural language search query for source-code recommendations, identifying source code from the augmented programming language corpus responsive to the natural language search query, and responding to the natural language search query with the identified source code.

Abstract: A method of automated ticket resolution comprises training and testing feature-specific classifier models using ticket database records. The feature-specific classifier models include machine-learning-based classification models related to features of a ticket system. The method includes training and testing feature-specific solution models using resolved ticket solution database records. The feature-specific classifier models include machine-learning-based solution models related to the features. The method includes receiving a ticket inquiry including a ticket indicative of an issue related to the features, generating a problem statement representative of the issue using the tested classifier models, and communicating the problem statement to the tested solution models. The method includes predicting a solution to the problem statement by using the tested solution models. The solution includes directions to resolve the ticket.

Abstract: According to an aspect of an embodiment, operations include receiving first log data including first information associated with electronic trigger events from a trigger data source. The operations further include extracting trigger output parameters from a first sentence token of the received first log data and displaying a first set of trigger rules onto an electronic User Interface (UI) of a user device based on the extracted trigger output parameters. The operations further include receiving a first input comprising a selection of one or more trigger rules of the displayed first set of trigger rules and receiving a second input comprising a selection of computer-executable actions to be performed based on whether the first information satisfies the one or more trigger rules. The operations further include generating a computer-executable mashup code for execution on a runtime system based on the received first input and the received second input.

Abstract: A method may include obtaining one or more software-repository packages. A programming-language function may be extracted from the one or more software-repository packages. A curation resource associated with the programming-language function may be identified. The curation resource may include descriptive information related to the programming-language function. The method may include generating a code description corresponding to the programming-language function based on the curation resource. A function-comment pair that includes the programming-language function and the generated code description may be determined. A programming language corpus that includes the one or more software-repository packages may be generated and augmented by the function-comment pair. The method may include training a machine learning model using the programming language corpus.

Abstract: A method may include obtaining training code and extracting features from the training code. The extracted features of the training code may be mapped to natural language code vectors by a deep neural network. A natural language search query requesting source-code suggestions may be received, and the natural language search query may be mapped to a natural language search vector by the deep neural network. The method may include mapping the natural language search query to the natural language search vector in the same or a similar method as mapping the extracted features of the training code to natural language code vectors, and the natural language search vector may be compared to the natural language code vectors. Source code responsive to the natural language search query may be suggested based on the comparison between the natural language search vector and the natural language code vectors.

Abstract: A method to forecast a spike price of a commodity may include obtaining a request to perform a prediction of a spike in a price of a commodity. The method may also include obtaining additional information of the commodity based on the input. The method may also include determining, using a first machine learning algorithm, a probability of the spike based at least in part on the additional information. The method may include outputting the probability of the spike for visual representation on an electronic display in response to a determination that the probability of the spike is above a risk tolerance. The method may further include determining, using a second machine learning algorithm, a price prediction in response to a determination that the probability of the spike is below the risk tolerance, wherein determining the price prediction comprises outputting the price prediction for visual representation on the electronic display.

Abstract: A method may include obtaining multiple tasks. The multiple tasks may be configured to perform at least one function when executed. The method may also include obtaining an execution arrangement of a set of tasks of the multiple tasks. The execution arrangement may define when execution of functions of tasks of the set of tasks occur and may define data to be passed between at least two tasks of the set of tasks. The method may also include automatically generating a specification that includes a different object for each of the tasks of the set of tasks. The method may further include automatically generating executable code using the specification. The executable code may be configured to perform all the functions of all of the tasks of the set of tasks according to the execution arrangement based on a single user request.