Abstract: Aspects and elements of the present disclosure relate to systems and methods for determining whether a user of an application is a bot, the sequences of computer-executable instructions including instructions that instruct at least one processor to assign the user to a user classification of a plurality of user classifications for the application, provide, responsive to assigning the user to the user classification, one or more challenges of a plurality of challenges to the user over multiple instances of the user using the application, each challenge of the plurality of challenges being configured to determine whether the user is a bot, and each challenge being associated with at least one user classification of the plurality of user classifications, and change the user classification based on the user's response to the one or more challenges.

Abstract: A sensing and stimulation system includes a central hub, and a plurality of flexible arms extending from the central hub. Each of the arms includes at least one electrode and at least one sensor. Each of the arms is configured to perform sensing and stimulation including electrically stimulating biological material, and sensing biological responses and changes. The system includes a port configured to be alternatively connected to a remote control module for wireless operation of the system and a leaded connector for wired operation of the system.

Abstract: A work machine may include a steering system, a user interface, a detection device, and a control device. The user interface may generate an input signal corresponding to operator input. The detection device may generate a detection signal corresponding to a presence of an operator relative to the user interface. The control device may determine that the operator is unavailable based on the detection signal and the input signal, and enable a lockout mode based on determining that the operator is unavailable. The control device may determine, while the lockout mode is enabled, a readiness indicator. The readiness indicator may confirm a readiness of the operator based on determining that the detection signal satisfies a readiness criterion. The control device may disable the lockout mode based on determining that the readiness criterion is satisfied.

Abstract: A system may include multiple personal data sources and a machine-implemented data extractor and correlator configured to retrieve personal data from at least one of the personal data sources. The data extractor and correlator may extract information from unstructured data within the retrieved personal data and correlate the extracted information with previously stored structured data to generate additional structured data. The system may also include a storage device configured to store the previously stored structured data and the additional structured data. A natural language query module may be configured to receive a natural language query from a user and provide a response to the natural language query based at least in part on one or both of the previously stored structured data and the additional structured data.

Abstract: A system may include multiple personal data sources and a machine-implemented data extractor and correlator configured to retrieve personal data from at least one of the personal data sources. The data extractor and correlator may extract information from unstructured data within the retrieved personal data and correlate the extracted information with previously stored structured data to generate additional structured data. The system may also include a storage device configured to store the previously stored structured data and the additional structured data. A natural language query module may be configured to receive a natural language query from a user and provide a response to the natural language query based at least in part on one or both of the previously stored structured data and the additional structured data.

Abstract: A work machine may include a steering system, a user interface, a detection device, and a control device. The user interface may generate an input signal corresponding to operator input. The detection device may generate a detection signal corresponding to a presence of an operator relative to the user interface. The control device may determine that the operator is unavailable based on the detection signal and the input signal, and enable a lockout mode based on determining that the operator is unavailable. The control device may determine, while the lockout mode is enabled, a readiness indicator. The readiness indicator may confirm a readiness of the operator based on determining that the detection signal satisfies a readiness criterion. The control device may disable the lockout mode based on determining that the readiness criterion is satisfied.

Abstract: A sensing and stimulation system includes a central hub, and a plurality of flexible arms extending from the central hub. Each of the arms includes at least one electrode and at least one sensor. Each of the arms is configured to perform sensing and stimulation including electrically stimulating biological material, and sensing biological responses and changes. The system includes a port configured to be alternatively connected to a remote control module for wireless operation of the system and a leaded connector for wired operation of the system.

Abstract: A sensing and stimulation system includes a central hub, and a plurality of flexible arms extending from the central hub. Each of the arms includes at least one electrode and at least one sensor. Each of the arms is configured to perform sensing and stimulation including electrically stimulating biological material, and sensing biological responses and changes. The system includes a port configured to be alternatively connected to a remote control module for wireless operation of the system and a leaded connector for wired operation of the system.

Abstract: A system may include multiple personal data sources and a machine-implemented data extractor and correlator configured to retrieve personal data from at least one of the personal data sources. The data extractor and correlator may extract information from unstructured data within the retrieved personal data and correlate the extracted information with previously stored structured data to generate additional structured data. The system may also include a storage device configured to store the previously stored structured data and the additional structured data. A natural language query module may be configured to receive a natural language query from a user and provide a response to the natural language query based at least in part on one or both of the previously stored structured data and the additional structured data.

Abstract: A system may include multiple personal data sources and a machine-implemented data extractor and correlator configured to retrieve personal data from at least one of the personal data sources. The data extractor and correlator may extract information from unstructured data within the retrieved personal data and correlate the extracted information with previously stored structured data to generate additional structured data. The system may also include a storage device configured to store the previously stored structured data and the additional structured data. A natural language query module may be configured to receive a natural language query from a user and provide a response to the natural language query based at least in part on one or both of the previously stored structured data and the additional structured data.

Abstract: A system may include multiple personal data sources and a machine-implemented data extractor and correlator configured to retrieve personal data from at least one of the personal data sources. The data extractor and correlator may extract information from unstructured data within the retrieved personal data and correlate the extracted information with previously stored structured data to generate additional structured data. The system may also include a storage device configured to store the previously stored structured data and the additional structured data. A natural language query module may be configured to receive a natural language query from a user and provide a response to the natural language query based at least in part on one or both of the previously stored structured data and the additional structured data.

Abstract: A headboard for use with a bed is described. The headboard includes a panel having a top edge, and a light fixture proximate to the top edge. The light fixture can move relative to the panel between a first position and a second position. In the first position, the light fixture emits light in an upward direction away from the bed. In the second position, the light fixture emits light in a downward direction toward the bed.

Abstract: A headboard for use with a bed is described. The headboard includes a panel having a top edge, and a light fixture proximate to the top edge. The light fixture can move relative to the panel between a first position and a second position. In the first position, the light fixture emits light in an upward direction away from the bed. In the second position, the light fixture emits light in a downward direction toward the bed.

Abstract: A machine-implemented method for supporting a natural language user request against a user's personal data cloud can include a machine receiving the natural language user request from the user, determining a semantic interpretation of the natural language user request, querying a semantically-indexed, integrated knowledge store based on the semantic interpretation, and responding to the natural language user request by displaying results of the querying, wherein the results correspond to an item within the user's personal data cloud.

Abstract: A machine-implemented method for supporting a natural language user request against a user's personal data cloud can include a machine receiving the natural language user request from the user, determining a semantic interpretation of the natural language user request, querying a semantically-indexed, integrated knowledge store based on the semantic interpretation, and responding to the natural language user request by displaying results of the querying, wherein the results correspond to an item within the user's personal data cloud.

Abstract: A system may include multiple personal data sources and a machine-implemented data extractor and correlator configured to retrieve personal data from at least one of the personal data sources. The data extractor and correlator may extract information from unstructured data within the retrieved personal data and correlate the extracted information with previously stored structured data to generate additional structured data. The system may also include a storage device configured to store the previously stored structured data and the additional structured data. A natural language query module may be configured to receive a natural language query from a user and provide a response to the natural language query based at least in part on one or both of the previously stored structured data and the additional structured data.

Abstract: A machine-implemented method for supporting a natural language user request against a user's personal data cloud can include a machine receiving the natural language user request from the user, determining a semantic interpretation of the natural language user request, querying a semantically-indexed, integrated knowledge store based on the semantic interpretation, and responding to the natural language user request by displaying results of the querying, wherein the results correspond to an item within the user's personal data cloud.

Abstract: A system may include multiple personal data sources and a machine-implemented data extractor and correlator configured to retrieve personal data from at least one of the personal data sources. The data extractor and correlator may extract information from unstructured data within the retrieved personal data and correlate the extracted information with previously stored structured data to generate additional structured data. The system may also include a storage device configured to store the previously stored structured data and the additional structured data. A natural language query module may be configured to receive a natural language query from a user and provide a response to the natural language query based at least in part on one or both of the previously stored structured data and the additional structured data.

Abstract: A sensing and stimulation system includes a central hub, and a plurality of flexible arms extending from the central hub. Each of the arms includes at least one electrode and at least one sensor. Each of the arms is configured to perform sensing and stimulation including electrically stimulating biological material, and sensing biological responses and changes. The system includes a port configured to be alternatively connected to a remote control module for wireless operation of the system and a leaded connector for wired operation of the system.

Abstract: A computing system has a user interface allowing a user to view and input data related to concepts in a knowledge area associated with the user, an observation sub-system to centralize data and to identify a plurality of knowledge concepts, a conceptualization sub-system to generate a plurality of nodes within an ontological mapping, wherein each of the nodes corresponds to a certain one of the plurality of identified knowledge concepts identified by the observation sub-system, a relationship identification sub-system to create relationships between at least some of the plurality of identified knowledge concepts, and attribute affinity weights to the relationships, a change refinement sub-system to modify at least one of the plurality of nodes, affinity weights and relationships based upon information associated with the user, and a non-transitory knowledge store to store the information associated with the user pertaining to a sub-plurality of the plurality of identified knowledge concepts.