Abstract: Embodiments include systems, methods, and computer program products for monitoring progression of Parkinson's disease. Aspects include receiving pressure data from a plurality of pressure sensors, the pressure sensors being positioned on a chair. Aspects also include analyzing the pressure data to determine the severity of a unified Parkinson's disease rating scale factor for a patient.

Abstract: A method, system, and/or computer program product ameliorates mosquito populations. A flying drone is deployed over an area. Sensor readings that identify a presence of water in the area are received, and one or more processors determine a confidence level L that the water in the area is stagnant water. The flying drone is then directed to perform an amelioration action against the mosquito larvae based a value of the determined confidence level L.

Abstract: An embodiment of the invention provides a method for sending a message from a first user to a second user where an interface receives a message from the second user. A processor connected to the interface identifying one or more keywords in the message and searches an electronic database for the keyword(s) to identify one or more scripted message for the keyword(s). The electronic database includes scripted messages for each keyword in the electronic database. A communications device connected to the processor sends the identified scripted message(s) to the second user.

Abstract: An embodiment of the invention provides a method for sending a message from a first user to a second user where an interface receives a message from the second user. A processor connected to the interface identifying one or more keywords in the message and searches an electronic database for the keyword(s) to identify one or more scripted message for the keyword(s). The electronic database includes scripted messages for each keyword in the electronic database. A communications device connected to the processor sends the identified scripted message(s) to the second user.

Abstract: A method, system, and/or computer program product ameliorates mosquito populations. A flying drone is deployed over an area. Sensor readings that identify a presence of water in the area are received, and one or more processors determine a confidence level L that the water in the area is stagnant water. The processor(s) also determine a risk R of mosquito larvae being present in the stagnant water based on the confidence level L. The flying drone is then directed to perform an amelioration action against the mosquito larvae based on the values of L and R.

Abstract: A method for detecting and notifying of various potential hazards may include receiving a plurality of biometric data values by a first set of sensors of a sensing device. The biometric data values may correspond to a health state of a person. A plurality of environmental data values may be received by a second set of sensors of the sensing device. The environmental data values may correspond to a safety state of an environment that the person is in. The sensing device may compare the plurality of biometric data and environmental data values against respective models of biometric and environmental threshold data values that indicate a likelihood of a person or environment in an unhealthy or unsafe state. The sensing device may notify one or more communication devices if at least one of the biometric or environmental data values exceeds at least one of the threshold data values.

Abstract: A database comprises historical information of a user's response to previous notifications. The database is accessed to determine a time at which to provide a (new) notification to the user, utilizing at least: a) current user activity status (e.g., determined from measurement information collected from one or more personal devices and/or user calendar events; b) time/day; and c) context information about the notification (e.g., geo-location, indoors/outdoors) including notification type (e.g., calendar entry, email, IM). The user gets the notification via a portable device at the determined time. A machine learning model can select the determined time by discriminating features of the previous notifications for which the user immediately attended versus those that were deferred and/or ignored. Content of the notification can also be altered in view of such discriminating features so as to increase a likelihood the user will immediately attend to the provided notification.

Abstract: Controlling drones and vehicles in package delivery, in one aspect, may include routing a delivery vehicle loaded with packages to a dropoff location based on executing on a hardware processor a spatial clustering of package destinations. A set of drones may be dispatched. A drone-to-package assignment is determined for the drones and the packages in the delivery vehicle. The drone is controlled to travel from the vehicle's dropoff location to transport the assigned package to a destination point and return to the dropoff location to meet the vehicle. The delivery vehicle may be alerted to speed up or slow down to meet the drone at the return location, for example, without the delivery vehicle having to stop and wait at the dropoff location while the drone is making its delivery.

Abstract: A method for applying GPS UAV attitude estimation to accelerate computer vision. The UAV has a plurality of GPS receivers mounted at fixed locations on the UAV. The method includes receiving raw GPS measurements from each GPS satellite in view of the UAV, the raw GPS measurements comprising pseudo-range and carrier phase data representing the distance between each GPS receiver and each GPS satellite. Carrier phase and pseudo-range measurements are determined for each GPS receiver based on the pseudo-range and carrier phase data. The GPS carrier phase and pseudo-range measurements are compared pair-wise for each pair of GPS receiver and satellite. An attitude of the UAV is determined based on the relative distance measurements. A 3D camera pose rotation matrix is determined based on the attitude of the UAV. Computer vision image search computations are performed for analyzing the image data received from the UAV in real time using the 3D camera pose rotation matrix.

Abstract: Verifying a set of safety equipment criteria may include receiving a first unit of data at a first computing device. The first unit of data may be received via a wireless signal between the first computing device and a set of sensing devices. A set of safety equipment that includes the set of sensing devices may be identified as the first unit of data. The set of sensing devices may be configured to transmit the first unit of data. The wireless signal between the first computing device and the set of sensing devices may be monitored and the signal strength for the wireless signal may be determined to be below a threshold value. A second computing device may be notified in response to the determining that the signal strength for the wireless signal is below the threshold value.

Abstract: A method of providing a user interface with recipient status information, in one aspect, may comprise detecting a message (e.g., online message such as instant messaging, chat, etc.) being initiated by a first user to a second user; gathering information associated with the second user; analyzing the gathered information; predicting a state of the second user based on the analyzing; and determining a notification action based on the predicted state of the second user, the notification action notifying the first user of the second user's state; and presenting a notification comprising one or more of graphical, textual, auditory, or tactile indications or combinations thereof to the first user.

Abstract: A method of providing a user interface with recipient status information, in one aspect, may comprise detecting a message (e.g., online message such as instant messaging, chat, etc.) being initiated by a first user to a second user; gathering information associated with the second user; analyzing the gathered information; predicting a state of the second user based on the analyzing; and determining a notification action based on the predicted state of the second user, the notification action notifying the first user of the second user's state; and presenting a notification comprising one or more of graphical, textual, auditory, or tactile indications or combinations thereof to the first user.

Abstract: A method for controlling a drone includes receiving a natural language request for information about a spatial location, parsing the natural language request into data requests, configuring a flight plan and controlling one or more drones to fly over the spatial location to obtain data types based on the data requests, and extracting and analyzing data to answer the request. The method can include extracting data points from the data types, obtaining labels from a user for one or more of the data points, predicting labels for unlabeled data points from a learning algorithm using the labels obtained from the user, determining the predicted labels are true labels for the unlabeled data points and combining the extracted data, the user labeled data points and the true labeled data points to answer the request for information. The learning algorithm may be active learning using a support vector machine.

Abstract: Embodiments include methods, systems and computer program products for providing information to two or more people who know each other and who are running separately yet relatively near to each other at the same instance in time to come together and thereafter run together are provided. Aspects include determining a movement state and a location of a first user, and determining a movement state and a location of at least one other user. Aspects also include based on the location of the first user and the location of the at least one other user being within a predetermined distance from one another, determining a route for each of the first user and the at least one other user to travel to come together at a single geographic location.

Abstract: A system and computer product for validating the consistency between quantitative and natural language textual evaluations. An example method involves computing a numeric score for a textual evaluation, comparing the numeric score to a quantitative evaluation, and producing a rating based on the similarity of the two evaluations.

Abstract: A system and computer product for validating the consistency between quantitative and natural language textual evaluations. An example method involves computing a numeric score for a textual evaluation, comparing the numeric score to a quantitative evaluation, and producing a rating based on the similarity of the two evaluations.

Abstract: A method for controlling a drone includes receiving a natural language request for information about a spatial location, parsing the natural language request into data requests, configuring a flight plan and controlling one or more drones to fly over the spatial location to obtain data types based on the data requests, and extracting and analyzing data to answer the request. The method can include extracting data points from the data types, obtaining labels from a user for one or more of the data points, predicting labels for unlabeled data points from a learning algorithm using the labels obtained from the user, determining the predicted labels are true labels for the unlabeled data points and combining the extracted data, the user labeled data points and the true labeled data points to answer the request for information. The learning algorithm may be active learning using a support vector machine.

Abstract: A method for detecting and notifying of various potential hazards may include receiving a plurality of biometric data values by a first set of sensors of a sensing device. The biometric data values may correspond to a health state of a person. A plurality of environmental data values may be received by a second set of sensors of the sensing device. The environmental data values may correspond to a safety state of an environment that the person is in. The sensing device may compare the plurality of biometric data and environmental data values against respective models of biometric and environmental threshold data values that indicate a likelihood of a person or environment in an unhealthy or unsafe state. The sensing device may notify one or more communication devices if at least one of the biometric or environmental data values exceeds at least one of the threshold data values.

Abstract: A method, executed by one or more processors, includes monitoring a plurality of ambient condition sensors configured to sense a plurality of ambient conditions that are relevant to a plurality of communication modes provided by at least one user I/O device, deactivating a first communication mode of the plurality of communication modes and activating a second communication mode of the plurality of communication modes in response to determining, via the plurality of ambient condition sensors, that one or more of the plurality of ambient conditions are not conducive to the first communication mode and that the plurality of ambient conditions are collectively more conducive to the second communication mode than the first communication mode. A corresponding apparatus and computer program product are also disclosed herein.

Abstract: Verifying a set of safety equipment criteria may include receiving a first unit of data at a first computing device. The first unit of data may be received via a wireless signal between the first computing device and a set of sensing devices. A set of safety equipment that includes the set of sensing devices may be identified as the first unit of data. The set of sensing devices may be configured to transmit the first unit of data. The wireless signal between the first computing device and the set of sensing devices may be monitored and the signal strength for the wireless signal may be determined to be below a threshold value. A second computing device may be notified in response to the determining that the signal strength for the wireless signal is below the threshold value.