Abstract: In some examples, user journey carbon footprint reduction may include generating, for a vehicle associated with a user, a carbon emission quota for user journey carbon footprint reduction. A predicted journey carbon emissions may be generated for the vehicle for a specified journey. Based on collaborative filtering, at least one goal-based and conditions-based recommendation may be generated for the user of the vehicle for the specified journey for the user journey carbon footprint reduction. Based on a user behavior model, a user-interface display may be generated for the specified journey for the user journey carbon footprint reduction. Further, based on the user behavior model, and real-time monitoring of the user and the vehicle, a real-time update of the user-interface display may be generated for the specified journey for the user journey carbon footprint reduction.

Abstract: In some examples, extended reality based immersive project workspace creation may include obtaining sensing data from at least one sensor of a device worn by a user, and analyzing, based on the sensing data, an environment of the user. A plurality of insights associated with the user may be determined, and prioritized based on a plurality of prioritization criteria. An insight of the plurality of prioritized insights associated with the user may be rendered in the environment of the user. Further, the rendering of the insight may be controlled based on an interaction of the user with the rendered insight.

Abstract: In some examples, software CoBot engineering, execution, and monitoring may include extracting a CoBot requirement from a requirement specification for a CoBot that is to be implemented. Based on application of a CoBot description language to the CoBot requirement, a CoBot workflow that specifies a plurality of tasks to be performed by the CoBot may be generated. A determination may be made as to whether a task is to be performed by a bot or by a human. A team that includes a plurality of bots and at least one human may be generated to execute the CoBot workflow. The bots of the team may be prioritized to identify a bot that is a best match to the CoBot requirement. The CoBot that includes configured bots may be deployed in an operational environment to perform the CoBot workflow.

Abstract: In some examples, software CoBot engineering, execution, and monitoring may include extracting a CoBot requirement from a requirement specification for a CoBot that is to be implemented. Based on application of a CoBot description language to the CoBot requirement, a CoBot workflow that specifies a plurality of tasks to be performed by the CoBot may be generated. A determination may be made as to whether a task is to be performed by a bot or by a human. A team that includes a plurality of bots and at least one human may be generated to execute the CoBot workflow. The bots of the team may be prioritized to identify a bot that is a best match to the CoBot requirement. The CoBot that includes configured bots may be deployed in an operational environment to perform the CoBot workflow.

Abstract: In some examples, user journey carbon footprint reduction may include generating, for a vehicle associated with a user, a carbon emission quota for user journey carbon footprint reduction. A predicted journey carbon emissions may be generated for the vehicle for a specified journey. Based on collaborative filtering, at least one goal-based and conditions-based recommendation may be generated for the user of the vehicle for the specified journey for the user journey carbon footprint reduction. Based on a user behavior model, a user-interface display may be generated for the specified journey for the user journey carbon footprint reduction. Further, based on the user behavior model, and real-time monitoring of the user and the vehicle, a real-time update of the user-interface display may be generated for the specified journey for the user journey carbon footprint reduction.

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: According to an example, customized visualization based intelligence augmentation may include accessing, based on a user request, a domain model, and mapping the user request to the domain model. Based on the mapping, a guided query that includes a relevant refinement question may be generated. A response may be received to the refinement question. Based on the received response, a refined user request may be generated, and classified into an intelligence augmentation category. Based on the classification, an intelligence augmentation analyzer may be accessed to analyze the refined user request to generate an insight output that is classified to a visualization. Based on the classification of the insight output to the visualization, responsive to the user request, a display of the visualization may be generated.

Abstract: In some examples, extended reality based positive affect implementation for product development may include determining, for a user wearing or utilizing a device, characteristics and an environment of the user. Based on the characteristics and the environment of the user, a plurality of augmentation elements may be determined. A recommendation of a plurality of augmentation elements from the determined plurality of augmentation elements may be generated. Based on selection of an augmentation element from the recommended plurality of augmentation elements, the augmentation element may be rendered in an extended environment and/or an immediate environment of the user. Interaction of the user may be controlled with the rendered augmentation element. Feedback of the user may be analyzed with respect to the rendered augmentation element.

Abstract: In some examples, extended reality based positive affect implementation for product development may include determining, for a user wearing or utilizing a device, characteristics and an environment of the user. Based on the characteristics and the environment of the user, a plurality of augmentation elements may be determined. A recommendation of a plurality of augmentation elements from the determined plurality of augmentation elements may be generated. Based on selection of an augmentation element from the recommended plurality of augmentation elements, the augmentation element may be rendered in an extended environment and/or an immediate environment of the user. Interaction of the user may be controlled with the rendered augmentation element. Feedback of the user may be analyzed with respect to the rendered augmentation element.

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: In some examples, extended reality based immersive project workspace creation may include obtaining sensing data from at least one sensor of a device worn by a user, and analyzing, based on the sensing data, an environment of the user. A plurality of insights associated with the user may be determined, and prioritized based on a plurality of prioritization criteria. An insight of the plurality of prioritized insights associated with the user may be rendered in the environment of the user. Further, the rendering of the insight may be controlled based on an interaction of the user with the rendered insight.

Abstract: According to an example, intent and bot based query guidance may include receiving a query associated with a domain, and identifying, based on an analysis of the query, an intent of the query by extracting an action associated with the query and an entity associated with the query. An intent model associated with the query may be generated based on a mapping of the action and the entity with a domain model of the domain. An intent domain specific language representation of the intent model associated with the query may be generated. Based on an analysis of the intent domain specific language representation, a plurality of bots may be identified, and a parameterized bot chain may be generated to respond to the query. A runtime binding of bots of the parameterized bot chain may be performed, and invoked to generate the response to the query.

Abstract: A device may instrument a codebase associated with a software application. The device may execute a test on the instrumented codebase as the instrumented codebase executes, wherein the instrumented codebase generates test data based on the test being executed. The device may generate, based on the test data, a live graphical model of the codebase from a composite graphical model of the codebase, wherein the composite graphical model includes historical information, associated with the codebase, mapped to a graphical model of the codebase. The device may generate an extended reality rendered view of the live graphical model. The device may provide, to an extended reality device, the extended reality rendered view of the live graphical model for display by the extended reality device.

Abstract: According to an example, customized visualization based intelligence augmentation may include accessing, based on a user request, a domain model, and mapping the user request to the domain model. Based on the mapping, a guided query that includes a relevant refinement question may be generated. A response may be received to the refinement question. Based on the received response, a refined user request may be generated, and classified into an intelligence augmentation category. Based on the classification, an intelligence augmentation analyzer may be accessed to analyze the refined user request to generate an insight output that is classified to a visualization. Based on the classification of the insight output to the visualization, responsive to the user request, a display of the visualization may be generated.

Abstract: According to an example, customized visualization based intelligence augmentation may include accessing, based on a user request, a domain model, and mapping the user request to the domain model. Based on the mapping, a guided query that includes a relevant refinement question may be generated. A response may be received to the refinement question. Based on the received response, a refined user request may be generated, and classified into an intelligence augmentation category. Based on the classification, an intelligence augmentation analyzer may be accessed to analyze the refined user request to generate an insight output that is classified to a visualization. Based on the classification of the insight output to the visualization, responsive to the user request, a display of the visualization may be generated.

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 instrument a codebase associated with a software application. The device may execute a test on the instrumented codebase as the instrumented codebase executes, wherein the instrumented codebase generates test data based on the test being executed. The device may generate, based on the test data, a live graphical model of the codebase from a composite graphical model of the codebase, wherein the composite graphical model includes historical information, associated with the codebase, mapped to a graphical model of the codebase. The device may generate an extended reality rendered view of the live graphical model. The device may provide, to an extended reality device, the extended reality rendered view of the live graphical model for display by the extended reality device.

Abstract: Recurrent gene fusions of androgen regulated genes and ETS family member genes in prostate cancer are described. Compositions and methods having utility in prostate cancer diagnosis, research, and therapy are also provided.

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: According to an example, intent and bot based query guidance may include receiving a query associated with a domain, and identifying, based on an analysis of the query, an intent of the query by extracting an action associated with the query and an entity associated with the query. An intent model associated with the query may be generated based on a mapping of the action and the entity with a domain model of the domain. An intent domain specific language representation of the intent model associated with the query may be generated. Based on an analysis of the intent domain specific language representation, a plurality of bots may be identified, and a parameterized bot chain may be generated to respond to the query. A runtime binding of bots of the parameterized bot chain may be performed, and invoked to generate the response to the query.