Abstract: A method, computer system, and a computer program product for interpreting conference call interruptions is provided. The present invention may include analyzing a conference call including at least two participants. The present invention may also include identifying at least one interrupted segment in the analyzed conference call. The present invention may further include reconstructing the identified at least one interrupted segment of the analyzed conference call and integrating it with the uninterrupted (heard) portion of the conference call.

Abstract: A method for measuring alpha particle emissions may include obtaining a wafer emission rate, wherein the wafer emission rate is measured with a counter. The method may further include covering the wafer with a metal mesh grounded to a cathode of the counter wherein the metal mesh is grounded to the cathode outboard of the wafer and obtaining a mesh and wafer emission rate, wherein the mesh and wafer emission rate is measured with the counter. The method may further include replacing the wafer with a wafer carcass, obtaining a wafer carcass and mesh emission rate, and calculating a wafer carcass emissivity.

Abstract: A computer-implemented method, system, and/or computer program product controls self-driving vehicles (SDVs). An emergency message is transmitted to a receiver within a self-driving vehicle (SDV). The emergency message describes an emergency state of an emergency vehicle and an identified future route of the emergency vehicle, where the identified future route is a planned route to an emergency destination for the emergency vehicle that includes a first pathway. In response to the SDV receiving the emergency message, the SDV is redirected, via an auto-control hardware system on the SDV, to drive to a second pathway that does not conflict with the identified future route of the emergency vehicle.

Abstract: A mobile electronic device such as a smartphone is used in conjunction with a deep learning system to detect and respond to personal danger. The deep learning system monitors current information (such as location, audio, biometrics, etc.) from the smartphone and generates a risk score by comparing the information to a routine profile for the user. If the risk score exceeds a predetermined threshold, an alert is sent to the smartphone which presents an alert screen to the user. The alert screen allows the user to cancel the alert (and notify the deep learning system) or confirm the alert (and immediately transmit an emergency message). Multiple emergency contacts can be designated, e.g., one for a low-level risk, another for an intermediate-level risk, and another for a high-level risk, and the emergency message can be sent to a selected contact depending upon the severity of the risk score.

Abstract: Methods and systems for predicting congestion duration are described. A processor can detect an occurrence of an incident in an area. The processor can receive context data associated with the area from at least one data source. The processor can execute a prediction engine using the received context data to predict a clearance time indicating a predicted completion time of post-incident activities related to the incident in the area. The processor can determine a congestion duration based on the clearance time. The congestion duration can be an estimated duration of congestion in the area in response to the occurrence of the incident. The processor can compare the congestion duration with a threshold. The processor can select, based on the comparison, at least one operation to optimize an amount of congestion in the area. The processor can execute the selected operations to optimize the amount of congestion in the 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 method steers a self-driving vehicle (SDV) onto a particular tire path on a roadway based on what type of tires are on the SDV. The method determines a pattern of tire path usage by vehicles on a roadway, and a type of tire upon which the SDV is riding. The method then directs an on-board computer on the SDV to drive the SDV on at least one specific tire path on the roadway based on the pattern of tire path usage by the vehicles on the roadway and the type of tire upon which the SDV is riding.

Abstract: A face mask and a method for fabricating a face mask. The face mask may comprise an air permeable membrane having an exterior surface and an interior surface, and a thermometer affixed to the exterior surface of the air permeable membrane. The air permeable membrane may be adapted to overlay a wearer's mouth and nose. The thermometer may be adapted to measure a temperature of the wearer and to allow other people in close proximity to the wearer see the temperature of the wearer at a glance.

Abstract: An approach to tree fall risk management. This approach may identify a tree in a given location. Historical data associated with the geographic location may be received in the approach. A current condition or status of the tree may be identified by the approach. The approach may analyze the foreseeable weather forecast or weather conditions in conjunction with the status of the identified tree. The approach may generate a risk score based on the information received and analyzed. The risk score may indicate the tree is likely to fall and cause damage. The approach may result in tree fall mitigation action can be generated based on the risk score.

Abstract: An operation performed with a computer cursor on a graphical user interface (GUI) can be detected, which operation copies content pointed to by the computer cursor into a memory buffer. An analysis can be performed to determine a risk associated with the operation. Based on the analysis, a change in a GUI element of the GUI can be made or caused.

Abstract: A method, system, and non-transitory compute readable medium for a fraud detection including a calendar reading device configured to read calendar data of a calendar for a user, a purchase detection device configured to detect an attempted purchase using a credit-card of the user, and a risk detection device configured to detect fraud by calculating a risk based on the calendar data and data related to the attempted purchase.

Abstract: An artificial neural network trained to predict the availability of an unused duration of a parking space based on input features is executed. Input features may include at least a contextual situation associated with the second entity, a behavior factor associated with a first entity that has been using the parking space, geographical location and time, events occurring within a threshold distance from the parking space. The artificial neural network may be further trained to output a transfer affinity based on the predicted availability of an unused duration, the contextual situation associated with the second entity and the behavior factor associated with the first entity. Based at least on the transfer affinity, the second entity can be selected. The unused duration can be transferred to the second entity from the first entity. The transferring can also include storing a payment and associated computation as a blockchain node in a blockchain.

Abstract: A method, system, and/or computer program product controls a self-driving vehicle (SDV) based on a purchase transaction at a point of sale (POS) device. One or more processors detects a purchase transaction at a device for a product. In response to detecting the purchase transaction at the POS device, the processors(s) transmits instructions to the SDV to transport a passenger associated with the purchase transaction from a location of the POS device to a predetermined location.

Abstract: A method and system for predicting vehicle wash services for a human-driven or self-driven vehicle. The method determines anticipated routes of travel for the vehicle based on context of a user associated with the vehicle, implements machine learning neural network models for predicting the road conditions in the anticipated routes of travel, and, then, auto-suggests one or more candidate car wash locations and associated Time of Days (to wash the car) while optimizing a user value.

Abstract: An exemplary method includes vehicle-mounted sensors continuously detecting vehicle speed and vehicle tire and steering vibrations; a processor implementing a machine-learning program that continuously monitors signals from the vehicle-mounted sensors and compares detected vehicle tire and steering vibrations to upper bounds corresponding to detected vehicle speed; and the processor alerting a vehicle driver that wheel or tire service is required based on the detected vehicle tire and steering vibrations exceeding the upper bounds. An exemplary apparatus includes a vehicle; tires mounted to the vehicle; a speed sensor mounted to the vehicle; a vibration sensor mounted to the vehicle; and a processor connected in communication with the speed sensor and the vibration sensor. The processor is adapted to implement any of the method steps above.

Abstract: A method, system, and/or computer program product controls operations of an aerial drone within a predetermined airspace. One or more processors detects that the aerial drone has entered a predetermined airspace, and also determines a physical size of the aerial drone. In response to detecting that the aerial drone has entered the predetermined airspace, one or more processors directs the aerial drone to alter a velocity of the aerial drone based on its physical size.

Abstract: A method, a structure, and a computer system for digital twin management. The exemplary embodiments may include gathering data corresponding to one or more bodily tissues and generating one or more digital twin versions of the one or more bodily tissues. In addition, the exemplary embodiments may further include identifying a most appropriate digital twin version of the one or more digital twin versions based on a health and a compatibility of the one or more digital twin versions, and distributing the most appropriate digital twin version.

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: Techniques facilitating mitigation of dangerous activities are provided. In one example, a computer-implemented method can include detecting, by a system operatively coupled to a processor, a trigger event based on a determination that a security risk level satisfies a defined condition associated with the security risk level. The security risk level can be associated with an item of interest and a monitored behavior related to the item of interest. The computer-implemented method can also include implementing, by the system, an action that mitigates an impact of the security risk level. The action can be implemented as the trigger event is detected and the action can be determined based on the item of interest and the monitored behavior.

Abstract: An example operation may include one or more of receiving user profile identification data associated with a user profile, creating a smart contract on a blockchain with the user profile identification data, validating an identity of the user profile based on the user profile identification data, performing a predictive analysis by the smart contract to determine one or more future tasks to be performed by a user device associated with the user profile, generating one or more tokens associated with the user profile, the one or more tokens include access rights for the user device to perform the one or more future tasks, and storing the one or more tokens in the blockchain.