Abstract: A computer-implemented method, according to one embodiment, includes: receiving copies of notes that were originally recorded by participants, the notes corresponding to a source document. Note fragments in the received copies of notes are identified and one or more machine learning models are used to compare the identified note fragments to pages and paragraphs in the source document that the respective note fragments are located in. Based at least in part on the comparison, the identified note fragments are merged with a copy of the source document to form a pseudo-source document. Furthermore, the pseudo-source document is output.

Abstract: A computer-implemented method, according to one approach, includes: receiving a request to add a subset of participants on a group call to a private communication channel. The group call includes a shared communication channel extending between the participants, the shared communication channel being configured to exchange information between the participants. The method further includes evaluating statuses of the participants in the subset. Moreover, in response to determining that the participants in the subset are not already included on a different private communication channel, and that the participants in the subset are not actively being added to a different private communication channel, the subset of participants are added to the private communication channel.

Abstract: A computer-implemented method, according to one embodiment, includes: receiving an initial sequence of operational instructions configured to update software programming in an electronic device. Contradicting ones of the operational instructions in the initial sequence are identified, and differences between the contradicting operational instructions are compared. As a result, the contradicting operational instructions are rectified by modifying the initial sequence of operational instructions. The software programming in the electronic device is also updated by implementing the modified sequence of operational instructions.

Abstract: A computer-implemented method includes receiving a data stream, the data stream including one or more driving factors. The computer-implemented method further includes comparing the one or more driving factors to one or more known driving factors to identify a driving scenario. The computer-implemented method further includes monitoring the driving scenario for a risk. Monitoring the driving scenario for a risk further includes detecting the risk. The computer-implemented method further includes generating an intervention, wherein the intervention is generated based on detecting the risk for the driving scenario. A corresponding computer system and computer program product are also disclosed.

Abstract: A computer-implemented method includes receiving a data stream, the data stream including one or more driving factors. The computer-implemented method further includes comparing the one or more driving factors to one or more known driving factors to identify a driving scenario. The computer-implemented method further includes monitoring the driving scenario for a risk. Monitoring the driving scenario for a risk further includes detecting the risk. The computer-implemented method further includes generating an intervention, wherein the intervention is generated based on detecting the risk for the driving scenario. A corresponding computer system and computer program product are also disclosed.

Abstract: A computer-implemented method includes receiving a data stream, the data stream including one or more driving factors. The computer-implemented method further includes comparing the one or more driving factors to one or more known driving factors to identify a driving scenario. The computer-implemented method further includes monitoring the driving scenario for a risk. Monitoring the driving scenario for a risk further includes detecting the risk. The computer-implemented method further includes generating an intervention, wherein the intervention is generated based on detecting the risk for the driving scenario. A corresponding computer system and computer program product are also disclosed.

Abstract: A method of reducing noise in a video signal which includes a plurality of video frames being composed of a plurality of pixels, the method comprising the steps of: comparing video information contained in a current video frame and a plurality of temporally adjacent video frames; selecting from the current video frame and the adjacent video frames the video information that according to a predetermined condition is likely to be correct for the current video frame; and finally assigning the selected video information to the current video frame to thereby produce a video frame wherein noise has been reduced.