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 method for correcting translation errors when performing GUI globalization testing is disclosed. In one embodiment, such a method takes a screenshot of a graphical user interface (GUI). The method further enables a user to select text in the screenshot. In certain embodiments, one or more of the text and coordinates associated with the text are stored in picture attributes associated with the screenshot. In response to the selection, the method automatically retrieves a file in which at least some portion of the text is contained. The method automatically locates, within the file, the portion, and enables the user to update the portion in the file in order to update corresponding text in the graphical user interface. In certain embodiments, the method provides a probability percentage associated with the file that indicates a probability that the text in the file links to the selected text in the graphical user interface.

Abstract: A method, computer system, and a computer program product are provided for a restoring missing content to a user that was absent during a portion of a live event. The process will identify the absence period and that is being participated by a user and obtains extracted information relating to the user that was previously gathered from a variety of sources including past user interactions and a user profile. This information is further analyzed and prioritized according to user interest priority determined by the extracted information. This information is categorized into a first set of domains. Information provided during the absence of the user will also be captured and categorized into a second set of domain categories. An intersection between the first and second set of domain categories will be found and prioritized by user interest. This will be presented to the user upon user's return.

Abstract: A computer-implemented method for fording depth estimation based on a real-time image recognition is provided according an embodiment of the present disclosure. In the method, a first image of a low-lying road being submerged under a bridge can be acquired. At least one second image of the low-lying road without being submerged can be determined. A fording depth of the low-lying road can be estimated based on the first image and the at least one second image.

Abstract: A computer-implemented method for fording depth estimation based on a real-time image recognition is provided according an embodiment of the present disclosure. In the method, a first image of a low-lying road being submerged under a bridge can be acquired. At least one second image of the low-lying road without being submerged can be determined. A fording depth of the low-lying road can be estimated based on the first image and the at least one second image.