Abstract: Techniques for adjusting user experiences for participants of a multiuser session by deploying vocal-characteristic models to analyze audio streams received in association with the participants are disclosed herein. The vocal-characteristic models are used to identify emotional state indicators corresponding to certain vocal properties being exhibited by individual participants. Based on the identified emotional state indicators, probability scores are generated indicating a likelihood that individual participants are experiencing a predefined emotional state. For example, a specific participant's voice may be continuously received and analyzed using a vocal-characteristic model designed to detect whether vocal properties are consistent with a predefined emotional state. Probability scores may be generated based on how strongly the detected vocal properties correlate with the vocal-characteristic model.

Abstract: Techniques for adjusting user experiences for participants of a multiuser session by deploying vocal-characteristic models to analyze audio streams received in association with the participants. The vocal-characteristic models are used to identify emotional state indicators corresponding to certain vocal properties being exhibited by individual participants. Based on the identified emotional state indicators, probability scores are generated indicating a likelihood that individual participants are experiencing a predefined emotional state. For example, a specific participant's voice may be continuously received and analyzed using a vocal-characteristic model designed to detect vocal properties are consistent with a predefined emotional state. Probability scores may be generated based on how strongly the detected vocal properties correlate with the vocal-characteristic model.

Abstract: Toxicity-shield modules are activated with respect to participants of a multiuser virtual environment to shield individual participants from toxic behaviors. The toxicity-shield modules may be customized for each participant of the multiuser virtual environment based on tolerances of each participant for exposure to predetermined toxic behavior(s). The toxicity-shield modules may be activated during the multiuser virtual environment to monitor communications data (e.g., voice-based and/or text-based “chats” and/or a video stream) between the participants and to manage the exposure level of individual participants to the predetermined toxic behavior(s).

Abstract: A method disclosed herein provides for escalation of machine learning content selection for content moderation use. The method includes requesting reaction feedback from users of an online social community platform in association with each of a number of user-provided content items appearing in the online social community platform. The reaction feedback is analyzed to identify a subset of the user-provided content items satisfying reaction consensus criteria, and content moderation logic is then trained based on the subset of content items identified from the analysis of the reaction feedback to facilitate selective implementation of content moderation actions based on the trained content moderation logic.

Abstract: A method and system for monitoring configuration information of computing devices is provided. The monitoring system generates a desired configuration monitor (“DCM”) document for each computing device that is to have its configuration monitored. The DCM document specifies configuration settings to be monitored along with rules that specify how to determine whether a configuration setting is correct. Each computing device may include a comparison engine that inputs the DCM document and applies the rules of the DCM document to determine which configuration settings of the computing device are not in compliance.

Abstract: A method and system for monitoring configuration information of computing devices is provided. The monitoring system generates a desired configuration monitor (“DCM”) document for each computing device that is to have its configuration monitored. The DCM document specifies configuration settings to be monitored along with rules that specify how to determine whether a configuration setting is correct. Each computing device may include a comparison engine that inputs the DCM document and applies the rules of the DCM document to determine which configuration settings of the computing device are not in compliance.

Abstract: A method and system for monitoring configuration information of computing devices is provided. The monitoring system generates a desired configuration monitor (“DCM”) document for each computing device that is to have its configuration monitored. The DCM document specifies configuration settings to be monitored along with rules that specify how to determine whether a configuration setting is correct. Each computing device may include a comparison engine that inputs the DCM document and applies the rules of the DCM document to determine which configuration settings of the computing device are not in compliance.

Abstract: A method and system for monitoring configuration information of computing devices is provided. The monitoring system generates a desired configuration monitor (“DCM”) document for each computing device that is to have its configuration monitored. The DCM document specifies configuration settings to be monitored along with rules that specify how to determine whether a configuration setting is correct. Each computing device may include a comparison engine that inputs the DCM document and applies the rules of the DCM document to determine which configuration settings of the computing device are not in compliance.