Abstract: Systems and methods that facilitate determining interaction between medications and the brain using a brain measure and a brain model. Hidden nervous system states are difficult to predict, diagnose, and treat with therapeutic medications. A Dual Neural Machine Translation (d-NMT) algorithmic system that utilizes sets of parameters for a relapsing-remitting MS model based on patient medical records and adjusts a method of parameterization to produce a model that can match patients' medical records and medical images. These parameters are can be used by a therapeutic determining model to recommend therapies, doses, and time courses accurately.

Abstract: A processor-implemented method records and maintains a record of browser events in a blockchain using a peer-to-peer network. One or more processors detect one or more browser events for a browser on a computer. The processor(s) transmit transactions associated with the browser event(s) to a peer-to-peer network of devices that create a blockchain, which includes one or more blocks that describe the browser event(s), where the blockchain records and maintains a record, of browser events, that includes records of uniform resource locators (URLs) browsed by a user, bookmarks added to the browser, and search terms searched by the user. The processor(s) perform a vulnerability analysis to determine how vulnerable the computer and the browser are to a malicious attack, and adjust a frequency of transmitting the transactions from the computer to the peer-to-peer network of devices according to how vulnerable the computer and the browser are to the malicious attack.

Abstract: A computer-implemented method, system, and/or computer program product controls a driving mode of a self-driving vehicle (SDV). One or more processors compare a control processor competence level of an on-board SDV control processor in controlling the SDV to a human driver competence level of a human driver in controlling the SDV while the SDV encounters a current roadway condition which is a result of current weather conditions of the roadway on which the SDV is currently traveling. One or more processors then selectively assign control of the SDV to the SDV control processor or to the human driver while the SDV encounters the current roadway condition based on which of the control processor competence level and the human driver competence level is relatively higher to one another.

Abstract: A time-management planning method, system, and computer program product, includes determining at least one of a cognitive state and a contextual state of a user, compiling a specification of a cognitive goal associated with accomplishing a scheduled task for a period of time, and performing a planning action, via a time management software platform, to adjust the scheduled task based on the specification of the cognitive goal and the at least one of the cognitive state and the contextual state of the user.

Abstract: A set of learner data associated with a learner is received. The set of learner data includes at least a real-time data stream, e.g., derived from instrumentation, in a social network enabled digital learning environment. Based on the set of learner data, at least one learning improvement strategy is determined for improving the learner's learning capability. Atomic level mentorship activities are generated. Each atomic level mentorship activity is targeted with a specific skill and knowledge item and having an estimated duration period. At least one mentorship is identified from at least one social network platform. An initial incentive structure comprising at least an amount of payment and payment modality is created. The atomic level mentorship activities with the initial incentive structure are presented.

Abstract: The method includes receiving a first set of data from a first client device. The method further includes determining a first topic from the first set of data. The method further includes generating a first productivity value for the first topic. The method further includes receiving a second set of date data from a second client device. The method further includes determining a second topic from by the second set of data. The method further includes generating a second productivity value for second set of data. The method further includes comparing the first topic to the second topic. The method further includes in response to comparing the first topic and the second topic and determining the difference between the first productivity value and the second productivity value is above a threshold value, triggering an action.

Abstract: An autonomous vehicle utilizing machine learning method, system, and computer program product include training, by a computing device, a driver attention model utilizing historical ambient sensor data and historical physiological sensor data associated with an autonomous vehicle, receiving ambient sensor data associated with a currently driving autonomous vehicle, receiving physiological sensor data associated with a driver of the currently driving autonomous vehicle, and using, by a computing device, the trained driver attention model in conjunction with the ambient sensor data and the physiological sensor data to generate an alert for a driver of the currently driving autonomous vehicle.

Abstract: Information relating to at least one of a user and a user environment is acquired. A user cognitive state is determined based on the acquired information. A graphical control element is automatically configured based on the user cognitive state. The graphical control element is automatically presented on a display interface of a user device to control viewing of content displayed on the user device.

Abstract: Method, system, and computer product for operating a self-driving vehicle (SDV). The SDV receives information from a calendar entry of a passenger's schedule about an event at a destination location to which the SDV is to drive the passenger to. The SDV sensors detect one or more behaviors or cognitive states of the passenger while in the vehicle. The SDV determines an optimal arrival time at the destination location based on the user's schedule information and the user's behavior or cognitive state; and further alters the driving speed or route taken by the SDV based on the determined optimal arrival time and an estimated travel time. The reshaping of the SDV driving characteristics is based on calendar information received including: an event meeting time, event topic, attendees, event importance. A detected passenger behavior can include talking on a phone, reading, or being in a tired, sleeping, agitated or nervous state.

Abstract: A content delivery method, system, and non-transitory computer readable medium, include determining an attentiveness factor in response to viewing of a media content and replaying the media content starting from a point that the attentiveness factor is determined to be less than a predetermined threshold.

Abstract: A method, system, and computer program product for weight adjusted composite model for forecasting in anomalous environments are provided in the illustrative embodiments. A base forecasting model and a second forecasting model are combined to form a composite model, the base forecasting model configured to forecast an event in a time series, the second forecasting model configured to represent an anomalous portion of data in the time series. A mixing algorithm is combined with the composite model to adjust a set of weights associated with the composite model. Upon identifying a future period in which the event is to be forecasted, using the mixing algorithm, a subset of the set of weights is adjusted to from a weight adjusted composite model. The weight adjusted composite model is executed to forecast the event in the future period.

Abstract: A cognitive state prediction method, system, and non-transitory computer readable medium, include providing an initial label associated with an electronic message, matching the initial label with a sender and data of the sender at a time of the electronic message, and predicting a second label associated with a second electronic message from the sender according to an analysis that compares a second data of the sender at a second time of the second electronic message with the data of the sender at the time of the electronic message.

Abstract: Embodiments of the present invention describe predictively reconstructing a physical event using augmented reality. Embodiments describe, identifying relative states of objects located in a physical event area by using video analysis to analyze collected video feeds from the physical event area before and after a physical event involving at least one of the objects, creating a knowledge corpus including the video analysis and the collected video feeds associated with the physical event and historical information, and capturing data, by a computing device, of the physical event area.

Abstract: A computer-implemented method, system, and/or computer program product controls a driving mode of a self-driving vehicle (SDV). One or more processors compare a control processor competence level of an on-board SDV control processor in controlling the SDV to a human driver competence level of a human driver in controlling the SDV while the SDV encounters a current roadway condition which is a result of current weather conditions of the roadway on which the SDV is currently traveling. One or more processors then selectively assign control of the SDV to the SDV control processor or to the human driver while the SDV encounters the current roadway condition based on which of the control processor competence level and the human driver competence level is relatively higher to one another.

Abstract: A cognitive state prediction method, system, and non-transitory computer readable medium, include a receiving circuit configured to receive an electronic message sent by a first user, a labeling circuit configured to query a second user to associate a label with the electronic message based on a cognitive state of the first user, and a correlating circuit configured to correlate the label with user data at a time of sending the electronic message, the user data corresponding to data output by at least one of the wearable and an external sensor in a database.

Abstract: Aspects of the present disclosure relate to an autonomous mobile attenuator system for mitigating vehicular collisions. The system includes one or more mobile attenuators that receive data indicating a need for deployment from one or more sensors. The one or more mobile attenuators perform a collision risk assessment on the received data to determine a probability of a potential vehicle collision. The one or more mobile attenuators determine the probability of the potential vehicle collision exceeds a predetermined risk threshold value. The one or more mobile attenuators determine a predicted location for the potential vehicle collision. The one or more mobile attenuators proceed to the predicted location to mitigate the potential vehicle collision.

Abstract: An exemplary computer-implemented method includes obtaining at least one teleportation invite block that records a virtual universe teleportation invite marked by at least one parameter. The teleportation invite identifies a virtual universe user as an invitee. Responsive to the parameter, assess whether the virtual universe teleportation invite is potentially malicious, and alert the invitee in case the virtual universe teleportation invite is potentially malicious.

Abstract: A method for operating a cognitive monitor includes measuring a cognitive tone of a user, determining a categorization of the cognitive tone of the user, selecting an effector using the categorization of the cognitive tone of the user, and applying the at least one effector using a feedback mechanism of the cognitive monitor. The effector is selected to achieve a predicted future cognitive tone in the user.

Abstract: Techniques regarding autonomously updating the status of a prescription are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise a prescription component that can update a status of a prescription associated with an entity based on a trust disposition value. The trust disposition value can be determined using machine learning technology and can be indicative of an expected effectiveness of the prescription.

Abstract: Techniques regarding autonomously updating a participation status of an entity with regards to a clinical trial are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise status component that can update a participation status of an entity regarding a clinical trial based on a trust disposition value. The trust disposition value can be determined using machine learning technology and can be indicative of an expected enrichment contribution associated with the participation status.