Abstract: Method and apparatus for identifying an advertisement based on a location of advertisements stored within an advertisement database. An advertisement engine is configured to generate a query comprising a geographical zone identified using user data received from a mobile device. The user data comprises a location and an orientation of the mobile device. The advertisement engine is further configured to identify one or more advertisements of the plurality of advertisements that are within the geographical zone by comparing the locations of the plurality of advertisements to the geographical zone. Further, the advertisement engine is configured to communicate information corresponding to the identified one or more advertisements to the mobile device.

Abstract: Automatically learning and providing software development team structure and methodologies. A software development knowledgebase repository is generated by mining for software processes data over a network of computer systems. A team structure specification and project requirement associated with a target project is received. A software development methodology is selected from the software development knowledgebase repository based on the team structure specification and project requirement associated with a target project, a team members knowledgebase, a teams knowledgebase, and a past projects knowledgebase. A machine learning module automatically learns a software development methodology to select. Based on the software development methodology, a software development environment infrastructure for the target project is built.

Abstract: Contextualized selection of components in one aspect may include generating a developer profile associated with a team member, for example, for all team members designated to work on a computer-implemented development project, and generating a component profile associated with a software component, for example, for all software components identified based on received requirement specification associated with the project. Applicability of a software component to the team members may be computed based on developer profiles and a component profile associated with the software component. Based on the applicability, components are selected and may be downloaded to build a team project component repository.

Abstract: A computer-implemented method includes detecting a name occurring in a component of a media playing on a first user endpoint device. The component is contained within an electronic signal. An entity in the media that is associated with the name is identified, and stored data that is contextually related to the entity is retrieved. The data is then formatted as supplemental content for display to a user of the first user endpoint device.

Abstract: A computer-implemented method includes detecting a name occurring in a component of a media playing on a first user endpoint device. The component is contained within an electronic signal. An entity in the media that is associated with the name is identified, and stored data that is contextually related to the entity is retrieved. The data is then formatted as supplemental content for display to a user of the first user endpoint device.

Abstract: Automatically learning and providing software development team structure and methodologies. A software development knowledgebase repository is generated by mining for software processes data over a network of computer systems. A team structure specification and project requirement associated with a target project is received. A software development methodology is selected from the software development knowledgebase repository based on the team structure specification and project requirement associated with a target project, a team members knowledgebase, a teams knowledgebase, and a past projects knowledgebase. A machine learning module automatically learns a software development methodology to select. Based on the software development methodology, a software development environment infrastructure for the target project is built.

Abstract: Contextualized selection of components in one aspect may include generating a developer profile associated with a team member, for example, for all team members designated to work on a computer-implemented development project, and generating a component profile associated with a software component, for example, for all software components identified based on received requirement specification associated with the project. Applicability of a software component to the team members may be computed based on developer profiles and a component profile associated with the software component. Based on the applicability, components are selected and may be downloaded to build a team project component repository.

Abstract: Various embodiments respond to a query in a cognitive decision-making system. In one embodiment, a query is received and a plurality of analytical tools defined in a knowledge base that are to be used to produce a response are identified. A workflow sequence is constructed based on a dependency graph of the identified analytical tools. Each analytical tool within the workflow sequence is executed to create a plurality of outputs. The workflow sequence is updated based on the outputs and the response is provided based on the outputs. At least one of a topic, an entity, a relationship and an unknown parameter within the query may be identified using natural language processing. The analytical tools may be digital tools or physical tools. Feedback on the response is received based on a confidence level and the knowledge base is updated based on the received feedback.

Abstract: Automatic driver modeling is used to integrate human-controlled vehicles into an autonomous vehicle network. A driver of a human-controlled vehicle is identified based on behavior patterns of the driver measured by one or more sensors of an autonomous vehicle. A model of the driver is generated based on the behavior patterns of the driver measured by the one or more sensors of the autonomous vehicle. Previously stored behavior patterns of the driver are then retrieved from a database to augment the model of the driver. The model of the driver is then transmitted from the autonomous vehicle to nearby vehicles with autonomous interfaces.

Abstract: Automatic driver modeling is used to integrate human-controlled vehicles into an autonomous vehicle network. A driver of a human-controlled vehicle is identified based on behavior patterns of the driver measured by one or more sensors of an autonomous vehicle. A model of the driver is generated based on the behavior patterns of the driver measured by the one or more sensors of the autonomous vehicle. Previously stored behavior patterns of the driver are then retrieved from a database to augment the model of the driver. The model of the driver is then transmitted from the autonomous vehicle to nearby vehicles with autonomous interfaces.

Abstract: Automatic driver modeling is used to integrate human-controlled vehicles into an autonomous vehicle network. A driver of a human-controlled vehicle is identified based on behavior patterns of the driver measured by one or more sensors of an autonomous vehicle. A model of the driver is generated based on the behavior patterns of the driver measured by the one or more sensors of the autonomous vehicle. Previously stored behavior patterns of the driver are then retrieved from a database to augment the model of the driver. The model of the driver is then transmitted from the autonomous vehicle to nearby vehicles with autonomous interfaces.

Abstract: A system configured to manage battery energy of a mobile device includes a primary mobile device and at least one peer device. The primary mobile device includes a power unit, a main communication module to electrically communicate with a peer device, and a peer assisted module in electrical communication with the power unit and the main communication module. The peer assisted module determines an energy level of the energy unit, and determines a task to be executed by the peer device. The peer device receives the task transmitted from the primary mobile device. The peer device further includes a peer process management module that executes at least one computation that completes the task, and communicates a completed task to the primary mobile device.

Abstract: A system configured to manage battery energy of a mobile device includes a primary mobile device and at least one peer device. The primary mobile device includes a power unit, a main communication module to electrically communicate with a peer device, and a peer assisted module in electrical communication with the power unit and the main communication module. The peer assisted module determines an energy level of the energy unit, and determines a task to be executed by the peer device. The peer device receives the task transmitted from the primary mobile device. The peer device further includes a peer process management module that executes at least one computation that completes the task, and communicates a completed task to the primary mobile device.

Abstract: Automatic driver modeling is used to integrate human-controlled vehicles into an autonomous vehicle network. A driver of a human-controlled vehicle is identified based on behavior patterns of the driver measured by one or more sensors of an autonomous vehicle. A model of the driver is generated based on the behavior patterns of the driver measured by the one or more sensors of the autonomous vehicle. Previously stored behavior patterns of the driver are then retrieved from a database to augment the model of the driver. The model of the driver is then transmitted from the autonomous vehicle to nearby vehicles with autonomous interfaces.

Abstract: Automatic driver modeling is used to integrate human-controlled vehicles into an autonomous vehicle network. A driver of a human-controlled vehicle is identified based on behavior patterns of the driver measured by one or more sensors of an autonomous vehicle. A model of the driver is generated based on the behavior patterns of the driver measured by the one or more sensors of the autonomous vehicle. Previously stored behavior patterns of the driver are then retrieved from a database to augment the model of the driver. The model of the driver is then transmitted from the autonomous vehicle to nearby vehicles with autonomous interfaces.

Abstract: A system configured to manage battery energy of a mobile device includes a primary mobile device and at least one peer device. The primary mobile device includes a power unit, a main communication module to electrically communicate with a peer device, and a peer assisted module in electrical communication with the power unit and the main communication module. The peer assisted module determines an energy level of the energy unit, and determines a task to be executed by the peer device. The peer device receives the task transmitted from the primary mobile device. The peer device further includes a peer process management module that executes at least one computation that completes the task, and communicates a completed task to the primary mobile device.

Abstract: A system configured to manage battery energy of a mobile device includes a primary mobile device and at least one peer device. The primary mobile device includes a power unit, a main communication module to electrically communicate with a peer device, and a peer assisted module in electrical communication with the power unit and the main communication module. The peer assisted module determines an energy level of the energy unit, and determines a task to be executed by the peer device. The peer device receives the task transmitted from the primary mobile device. The peer device further includes a peer process management module that executes at least one computation that completes the task, and communicates a completed task to the primary mobile device.

Abstract: Dynamic provisioning of resources is employed to replicate capabilities and/or services in a distributed computing infrastructure to overcome potential disruptions in the capabilities and/or services. Predictive tools for weather forecasts, risk profile analysis based on geographical location of data/service centers, and historical data are employed to improve service resiliency. Further, for each local computing service that is considered for replication, the cost of disruption is compared with the total cost of replication to ensure that a computing infrastructure service provider is selected in a cost-efficient manner.

Abstract: Dynamic provisioning of resources is employed to replicate capabilities and/or services in a distributed computing infrastructure to overcome potential disruptions in the capabilities and/or services. Predictive tools for weather forecasts, risk profile analysis based on geographical location of data/service centers, and historical data are employed to improve service resiliency. Further, for each local computing service that is considered for replication, the cost of disruption is compared with the total cost of replication to ensure that a computing infrastructure service provider is selected in a cost-efficient manner.