Abstract: In some implementations, a device may receive a configuration associated with a machine learning model. The device may additionally receive a first hyperparameter set associated with the machine learning model. Accordingly, the device may estimate a first quantity of floating-point operations (FLOPs) associated with one or more epochs, for the machine learning model, based on the first hyperparameter set. The device may output, to a user, an indication of a first energy consumption associated with training the machine learning model based on the first quantity of FLOPs.

Abstract: A device receives a machine learning model, model data associated with the machine learning model, and identifier generation data. The identifier generation data includes data utilized to generate identifier pairs that may be used to authenticate the machine learning model. The device selects an identifier model, for generating the identifier pairs, based on the machine learning model, the model data, and the identifier generation data. The device processes the machine learning model, the model data, and the identifier generation data, with the selected identifier model, to generate the identifier pairs and identifier pair data. The device stores the identifier pairs and the identifier pair data in one or more data structures, and utilizes the identifier pairs to identify and provide authentication for the machine learning model.

Abstract: A device may receive, from a user device, a request for an automation service for a project. The device may analyze the request to identify one or more input keywords. The device may obtain, by using the one or more input keywords to search one or more data sources, project information for a set of projects and/or automation services information for a set of automation services. The device may determine one or more similar projects and/or one or more similar automation services by analyzing the project information and/or the automation services information. The device may provide, to the user device, a recommendation that identifies one or more automation services associated with the one or more similar projects and/or the one or more similar automation services.

Abstract: A device may receive a process model and a reference process model. The process model may include a first plurality of nodes corresponding to a first plurality of stages of a particular process. The reference process model may include a second plurality of nodes corresponding to a second plurality of stages of a reference process. The device may map the process model to the reference process model, identify that a first stage corresponds to a second stage, determine a difference between a value, of a metric, associated with the first stage and a value, of the metric, associated with the second stage, cause an interactive user interface to display a visual representation of the difference, identify automation service(s) that compensate for the difference, determine a score for the process model, and cause the interactive user interface to display the score to enable selection of optimal automation service(s) to deploy.

Abstract: In some implementations, an advisor system may receive a description of a problem to be solved and problem data identifying quantum computing-related and classical computing-related problems. The advisor system may perform natural language processing on the description of the problem and the problem data to respectively generate a problem embedding vector for the problem and to generate embedding vectors that represent the quantum computing-related and classical computing-related problems. The advisor system may process the problem embedding vector and the embedding vectors, with a vector matching model, to determine a semantically closest matching one of the embedding vectors to the problem embedding vector and, accordingly, may generate a recommendation that includes an indication to solve the problem with a classical computing resource, a quantum computing resource, or a combination of a classical computing resource and a quantum computing resource.

Abstract: A device may obtain information identifying a base application. The device may extract a set of sub-application artifacts associated with the base application based on structural information associated with the base application. The device may define a set of metadata attributes associated with the set of sub-application artifacts associated with the base application. The device may generate a set of hash tuples for the set of metadata attributes associated with the set of sub-application artifacts associated with the base application. The device may generate a base composite identity of the base application based on the set of hash tuples. The device may store the base composite identity in a blockchain and in connection with storage of the base application in the blockchain to enable subsequent identification and verification of the base application.

Abstract: A device may receive a first sample. The device may identify a plurality of second samples that are similar to the first sample. The plurality of second samples may correspond to service tickets that have previously been processed. The device may obtain or determine log information identifying a plurality of events associated with the first sample. The device may determine respective probabilities of occurrence corresponding to the plurality of second samples. A probability of occurrence, for a particular second sample, may identify a likelihood of receiving a corresponding service ticket within a particular time period of occurrence of the plurality of events. The device may assign ranks to the plurality of second samples based on the respective probabilities of occurrence. The device may provide information identifying the ranks and the plurality of second samples.

Abstract: A device may receive information regarding a software environment and/or one or more modules associated with the software environment; receive information regarding one or more compliance requirements; determine one or more smart contracts based on the information regarding the one or more compliance requirements and the information regarding the software environment and/or the one or more modules associated with the software environment; obtain data regarding the software environment and/or the one or more modules; determine, using the one or more smart contracts, whether the data satisfies the one or more compliance requirements; and add at least part of a block, to a blockchain, to indicate whether the data satisfies the one or more compliance requirements, for realization of a reward or penalty associated with the one or more compliance requirements.

Abstract: A device may perform a static analysis of a codebase associated with a software application to determine a structure of the codebase and one or more metrics associated with the codebase. The device may generate a structured format of the codebase based on the structure of the codebase. The device may generate a domain specific language format of the codebase based on the structured format of the codebase and the one or more metrics associated with the codebase. The device may generate an extended reality rendered view of the domain specific language format of the codebase. The device may provide, to an extended reality device, the extended reality rendered view of the domain specific language format of the codebase.

Abstract: A device may evaluate first data to identify a plurality of users, where each user, of the plurality of users, is assigned to complete a portion of a group task. The device may evaluate the first data to select a first user, of the plurality of users, for completion of a particular portion of the group task based on a first set of decision criteria. The device may provide, to the first user, information identifying the particular portion of the group task. The device may receive second data relating to the particular portion of the group task after providing information identifying the particular portion of the group task. The device may evaluate the first data to select a second user, of the plurality of users, based on a second set of decision criteria. The device may provide, to the second user, information associated with the second data.

Abstract: A device receives a machine learning model, model data associated with the machine learning model, and identifier generation data. The identifier generation data includes data utilized to generate identifier pairs that may be used to authenticate the machine learning model. The device selects an identifier model, for generating the identifier pairs, based on the machine learning model, the model data, and the identifier generation data. The device processes the machine learning model, the model data, and the identifier generation data, with the selected identifier model, to generate the identifier pairs and identifier pair data. The device stores the identifier pairs and the identifier pair data in one or more data structures, and utilizes the identifier pairs to identify and provide authentication for the machine learning model.

Abstract: A device may obtain information identifying a base application. The device may extract a set of sub-application artifacts associated with the base application based on structural information associated with the base application. The device may define a set of metadata attributes associated with the set of sub-application artifacts associated with the base application. The device may generate a set of hash tuples for the set of metadata attributes associated with the set of sub-application artifacts associated with the base application. The device may generate a base composite identity of the base application based on the set of hash tuples. The device may store the base composite identity in a blockchain and in connection with storage of the base application in the blockchain to enable subsequent identification and verification of the base application.

Abstract: A device may receive, from a user device, an implicit search request identifying a resource accessed by a user of the user device. Based on properties defined in a user role and a rule identified in context configuration information, the device may determine an intent associated with the user and a context associated with the intent and the resource accessed by the user. The device may send, based on query options identified in search configuration information, a search query identifying search criteria associated with the context, and may receive a search response identifying available resources that satisfy the search criteria. The device may determine a probability, for each of the available resources, that the available resource matches the context, identify a portion of the search response based on the probabilities, and send the portion to the user device.

Abstract: A device may receive a process model and a reference process model. The process model may include a first plurality of nodes corresponding to a first plurality of stages of a particular process. The reference process model may include a second plurality of nodes corresponding to a second plurality of stages of a reference process. The device may map the process model to the reference process model, identify that a first stage corresponds to a second stage, determine a difference between a value, of a metric, associated with the first stage and a value, of the metric, associated with the second stage, cause an interactive user interface to display a visual representation of the difference, identify automation service(s) that compensate for the difference, determine a score for the process model, and cause the interactive user interface to display the score to enable selection of optimal automation service(s) to deploy.

Abstract: A device may perform a static analysis of a codebase associated with a software application to determine a structure of the codebase and one or more metrics associated with the codebase. The device may generate a structured format of the codebase based on the structure of the codebase. The device may generate a domain specific language format of the codebase based on the structured format of the codebase and the one or more metrics associated with the codebase. The device may generate an extended reality rendered view of the domain specific language format of the codebase. The device may provide, to an extended reality device, the extended reality rendered view of the domain specific language format of the codebase.

Abstract: A device may receive information regarding a software environment and/or one or more modules associated with the software environment; receive information regarding one or more compliance requirements; determine one or more smart contracts based on the information regarding the one or more compliance requirements and the information regarding the software environment and/or the one or more modules associated with the software environment; obtain data regarding the software environment and/or the one or more modules; determine, using the one or more smart contracts, whether the data satisfies the one or more compliance requirements; and add at least part of a block, to a blockchain, to indicate whether the data satisfies the one or more compliance requirements, for realization of a reward or penalty associated with the one or more compliance requirements.

Abstract: A device may receive, from a user device, a request for an automation service for a project. The device may analyze the request to identify one or more input keywords. The device may obtain, by using the one or more input keywords to search one or more data sources, project information for a set of projects and/or automation services information for a set of automation services. The device may determine one or more similar projects and/or one or more similar automation services by analyzing the project information and/or the automation services information. The device may provide, to the user device, a recommendation that identifies one or more automation services associated with the one or more similar projects and/or the one or more similar automation services.

Abstract: A system may include one or more server devices. The system may provide a set of questions, to a user of a user device, to characterize an Internet of things system. The system may obtain responses from the user of the user device associated with each of the set of questions. The system may automatically create an Internet of things system architecture that defines the Internet of things system by applying associated Internet of things system architecture rules to the responses.

Abstract: A method for adapting execution of an application on a mobile device may be performed by a mobile device including a processor and a memory. The method may include receiving an application context, a process context, and one other context. The method also includes analyzing at least one of the application context or the process context together with the one other context. The method also includes dynamically adapting execution of the application on the mobile device based on the analysis. Adapting execution of the application may include transferring processing related to the application to a backend server for processing.

Abstract: A system may include one or more server devices. The system may provide a set of questions, to a user of a user device, to characterize an Internet of things system. The system may obtain responses from the user of the user device associated with each of the set of questions. The system may automatically create an Internet of things system architecture that defines the Internet of things system by applying associated Internet of things system architecture rules to the responses.