Abstract: Systems and methods for automated aggregated content commenting are disclosed herein. Such a system can include a memory including a content database; an evaluation criteria database; and a comment database. The system can include at least one server. The at least one server can: receive a content identifier; receive a user identifier; provide a representation of the piece of content to the user; receive a comment from the user identifying a portion of the piece of content; generate a portion of the comment database linked to the user and the content; generate an entry in the portion of the comment database linked to the user and the content; and store the comment in the entry in the portion of the comment database.

Abstract: Systems and methods for automated aggregated content commenting are disclosed herein. Such a system can include a memory including a content database; an evaluation criteria database; and a comment database. The system can include at least one server. The at least one server can: receive a content identifier; receive a user identifier; provide a representation of the piece of content to the user; receive a comment from the user identifying a portion of the piece of content; generate a portion of the comment database linked to the user and the content; generate an entry in the portion of the comment database linked to the user and the content; and store the comment in the entry in the portion of the comment database.

Abstract: Generally, embodiments of the invention are directed to methods, computer readable medium, servers, and systems for deidentified access of data. The deidentified access is permitted with the use of an identifier that uniquely indicates an outcome, the coding of the identifier obscures unaided human interpretation of the outcome, and the identifier uniquely identifies data for remediating performance associated with future outcomes.

Abstract: Generally, embodiments of the invention are directed to methods, computer readable medium, servers, and systems for deidentified access of data. The deidentified access is permitted with the use of an identifier that uniquely indicates an outcome, the coding of the identifier obscures unaided human interpretation of the outcome, and the identifier uniquely identifies data for remediating performance associated with future outcomes.

Abstract: A distributed processing method is disclosed herein for evaluating student work product. The distributed processing system includes a server, a database server, and an application server that are interconnected via a network, and connected via the network to a plurality of independent processing units. The independent processing units can include an analysis engine that is machine learning capable, and thus uniquely completes its processing tasks. The server can provide one or several pieces of data to one or several of the independent processing units, can receive an analysis results from the one or several independent processing units, and can update the result based on a value scoring the machine learning of the independent processing unit.

Abstract: A file receiver receives an electronic structure file that includes structure-file data associated with a spatial arrangement and detects a content object for processing that includes content-object data. A file transformation engine transforms the structure-file data from the structure file into an electronic record. A rendering engine renders an image of the transformed structure-file data arranged in the spatial arrangement. An interface engine detects an input corresponding to specification of a position of a data segment. A parsing engine defines a segment-position specification indicative of the position. A template engine generates an electronic template that associates an identifier of the data segment with the segment-position specification and associates the electronic template with a template identifier. A record classifier determines that the content object corresponds to the template identifier.

Abstract: Generally, embodiments of the invention are directed to methods, computer readable medium, servers, and systems for deidentified access of data. The deidentified access is permitted with the use of an identifier that uniquely indicates an outcome, the coding of the identifier obscures unaided human interpretation of the outcome, and the identifier uniquely identifies data for remediating performance associated with future outcomes.

Abstract: Systems and methods for automated aggregated content commenting are disclosed herein. Such a system can include a memory including a content database; an evaluation criteria database; and a comment database. The system can include at least one server. The at least one server can: receive a content identifier; receive a user identifier; provide a representation of the piece of content to the user; receive a comment from the user identifying a portion of the piece of content; generate a portion of the comment database linked to the user and the content; generate an entry in the portion of the comment database linked to the user and the content; and store the comment in the entry in the portion of the comment database.

Abstract: A distributed processing system is disclosed herein. The distributed processing system includes a server, a database server, and an application server that are interconnected via a network, and connected via the network to a plurality of independent processing units. The independent processing units can include an analysis engine that is machine-learning-capable, and thus uniquely completes its processing tasks. The server can provide one or several pieces of data to one or several of the independent processing units, can receive analysis results from these one or several independent processing units, and can update the result based on a value characterizing the machine learning of the independent processing unit.

Abstract: Methods, systems, and computer program products are provided for recovering from false positives of malware detection. Malware signatures that are defective may be causing false positives during software scanning for malware. Such defective malware signatures may be detected (e.g., by user feedback, etc.) and revoked. Computers that are using the malware signatures to detect malware may be notified of the revoked signatures, and may be enabled to re-scan content identified as containing malware using malware signatures that do not include the revoked malware signatures. As such, if the content is determined during the re-scan to not be infected, the content may be re-enabled for usage on the computer (e.g., may be restored from quarantine storage).

Abstract: Systems and methods for automated aggregated content commenting are disclosed herein. Such a system can include a memory including a content database; an evaluation criteria database; and a comment database. The system can include at least one server. The at least one server can: receive a content identifier; receive a user identifier; provide a representation of the piece of content to the user; receive a comment from the user identifying a portion of the piece of content; generate a portion of the comment database linked to the user and the content; generate an entry in the portion of the comment database linked to the user and the content; and store the comment in the entry in the portion of the comment database.

Abstract: A distributed processing method is disclosed herein for evaluating student work product. The distributed processing system includes a server, a database server, and an application server that are interconnected via a network, and connected via the network to a plurality of independent processing units. The independent processing units can include an analysis engine that is machine learning capable, and thus uniquely completes its processing tasks. The server can provide one or several pieces of data to one or several of the independent processing units, can receive an analysis results from the one or several independent processing units, and can update the result based on a value scoring the machine learning of the independent processing unit.

Abstract: A distributed processing system is disclosed herein. The distributed processing system includes a server, a database server, and an application server that are interconnected via a network, and connected via the network to a plurality of independent processing units. The independent processing units can include an analysis engine that is machine-learning-capable, and thus uniquely completes its processing tasks. The server can provide one or several pieces of data to one or several of the independent processing units, can receive analysis results from these one or several independent processing units, and can update the result based on a value characterizing the machine learning of the independent processing unit.

Abstract: Generally, embodiments of the invention are directed to methods, computer readable medium, servers, and systems for deidentified access of data. The deidentified access is permitted with the use of an identifier that uniquely indicates an outcome, the coding of the identifier obscures unaided human interpretation of the outcome, and the identifier uniquely identifies data for remediating performance associated with future outcomes.

Abstract: The subject disclosure relates to antimalware scanning, and more particularly to offline antimalware scanning of a host environment via an alternate, known safe operating system. An offline scanning product obtains data previously written by the host environment online antimalware scanning tool, e.g., configuration data and antimalware signatures in shared data stores accessible to the offline and online products, and uses that data to perform the offline antimalware scan. The offline scanning product writes results information and any quarantined files to other shared data stores, whereby the online environment, when rebooted, has access to the information, such as for review and to upload telemetry information to an online service for analysis. Also described is offline replacement of operating system files that cannot be cleaned or removed when online.

Abstract: A distributed processing system is disclosed herein. The distributed processing system includes a server, a database server, and an application server that are interconnected via a network, and connected via the network to a plurality of independent processing units. The independent processing units can include an analysis engine that is machine learning capable, and thus uniquely completes its processing tasks. The server can provide one or several pieces of data to one or several of the independent processing units, can receive an analysis results from the one or several independent processing units, and can update the result based on a value characterizing the machine learning of the independent processing unit.

Abstract: An anti-malware program monitors the behavior of a system after a system restore to determine the likelihood of a hidden infection of malicious code still existing after the system restore. The anti-malware program observes the dynamic behavior of the system by monitoring conditions that are likely to signify the possibility of an infection thereby necessitating the need to initiate anti-malware detection. The anti-malware program may observe the restoration history, system settings, malware infection history, to determine the likelihood of an existing hidden infection after a system restore.

Abstract: A file receiver receives an electronic structure file that includes structure-file data associated with a spatial arrangement and detects a content object for processing that includes content-object data. A file transformation engine transforms the structure-file data from the structure file into an electronic record. A rendering engine renders an image of the transformed structure-file data arranged in the spatial arrangement. An interface engine detects an input corresponding to specification of a position of a data segment. A parsing engine defines a segment-position specification indicative of the position. A template engine generates an electronic template that associates an identifier of the data segment with the segment-position specification and associates the electronic template with a template identifier. A record classifier determines that the content object corresponds to the template identifier.

Abstract: A file receiver receives an electronic structure file that includes structure-file data associated with a spatial arrangement and detects a content object for processing that includes content-object data. A file transformation engine transforms the structure-file data from the structure file into an electronic record. A rendering engine renders an image of the transformed structure-file data arranged in the spatial arrangement. An interface engine detects an input corresponding to specification of a position of a data segment. A parsing engine defines a segment-position specification indicative of the position. A template engine generates an electronic template that associates an identifier of the data segment with the segment-position specification and associates the electronic template with a template identifier. A record classifier determines that the content object corresponds to the template identifier.

Abstract: A file receiver receives an electronic structure file that includes structure-file data associated with a spatial arrangement and detects a content object for processing that includes content-object data. A file transformation engine transforms the structure-file data from the structure file into an electronic record. A rendering engine renders an image of the transformed structure-file data arranged in the spatial arrangement. An interface engine detects an input corresponding to specification of a position of a data segment. A parsing engine defines a segment-position specification indicative of the position. A template engine generates an electronic template that associates an identifier of the data segment with the segment-position specification and associates the electronic template with a template identifier. A record classifier determines that the content object corresponds to the template identifier.