Abstract: Managing noise during an online conference session includes obtaining audio data from an endpoint participating in an online conference session. The audio data is derived from audio captured at the endpoint that includes musical sounds. The audio data is processed to identify a portion of the audio data in which a decibel level of the musical sounds is stable for a period of time. Non-musical noise present, if any, in the audio data with the musical sounds is identified and the non-musical noise is attenuated from the audio data to generate noise-reduced musical audio data. The noise-reduced musical audio data is transmitted for play out at one or more other endpoints participating in the online conference session.

Abstract: Managing noise during an online conference session includes obtaining audio data from an endpoint participating in an online conference session. The audio data is derived from audio captured at the endpoint that includes musical sounds. The audio data is processed to identify a portion of the audio data in which a decibel level of the musical sounds is stable for a period of time. Non-musical noise present, if any, in the audio data with the musical sounds is identified and the non-musical noise is attenuated from the audio data to generate noise-reduced musical audio data. The noise-reduced musical audio data is transmitted for play out at one or more other endpoints participating in the online conference session.

Abstract: A system and method for training a virtual assistant to recognize and learn new context for known terms is presented. The method includes receiving a natural language input, corresponding to at least one of a desired intent and a desired entity, at a natural language processor. The method involves scoring known intents based on the natural language input to generate an intent confidence score for each known intent, and scoring known entities based on the natural language input to generate an entity confidence score for each known entity. The method involves comparing the intent confidence scores and entity confidence scores to a threshold value, and determining that the natural language input does not correspond to at least one of the known intents and the known entities based on the comparing. Finally, at least one of a new intent and a new entity are determined based on the natural language input.

Abstract: Presented herein are techniques and systems associated with generating a predicted utilization likelihood for a shared collaboration resource. Integrated resource data associated with a meeting scheduled for a shared collaboration resource is obtained and analyzed using a machine-learned predictive model. The analysis generates a predicted utilization likelihood of the shared resource. An indication of the predicted utilization likelihood is provided to an output system, such as a graphical user interface.

Abstract: A system and method for training a virtual assistant to recognize and learn new context for known terms is presented. The method includes receiving a natural language input, corresponding to at least one of a desired intent and a desired entity, at a natural language processor. The method involves scoring known intents based on the natural language input to generate an intent confidence score for each known intent, and scoring known entities based on the natural language input to generate an entity confidence score for each known entity. The method involves comparing the intent confidence scores and entity confidence scores to a threshold value, and determining that the natural language input does not correspond to at least one of the known intents and the known entities based on the comparing. Finally, at least one of a new intent and a new entity are determined based on the natural language input.

Abstract: A system and method for authenticating a user via a single voice audio input is disclosed. The method includes obtaining voice audio from a user, converting the voice audio to text, comparing at least a portion of the text to a database, determining whether a user profile exists in the database based on the comparing, the user profile including a voiceprint, in response to determining that the user profile exists in the database, analyzing the voice audio against the voiceprint of the user profile; and authenticating the user based on the voice audio substantially matching the voiceprint of the user profile.

Abstract: Managing noise during an online conference session includes obtaining audio data from an endpoint participating in an online conference session. The audio data is derived from audio captured at the endpoint that includes musical sounds. The audio data is processed to identify a portion of the audio data in which a decibel level of the musical sounds is stable for a period of time. Non-musical noise present, if any, in the audio data with the musical sounds is identified and the non-musical noise is attenuated from the audio data to generate noise-reduced musical audio data. The noise-reduced musical audio data is transmitted for play out at one or more other endpoints participating in the online conference session.

Abstract: Presented herein are techniques and systems associated with generating a predicted utilization likelihood for a shared collaboration resource. Integrated resource data associated with a meeting scheduled for a shared collaboration resource is obtained and analyzed using a machine-learned predictive model. The analysis generates a predicted utilization likelihood of the shared resource. An indication of the predicted utilization likelihood is provided to an output system, such as a graphical user interface.

Abstract: Presented herein are techniques and systems associated with generating a predicted utilization likelihood for a shared collaboration resource. Integrated resource data associated with a meeting scheduled for a shared collaboration resource is obtained and analyzed using a machine-learned predictive model. The analysis generates a predicted utilization likelihood of the shared resource. An indication of the predicted utilization likelihood is provided to an output system, such as a graphical user interface.

Abstract: Presented herein are techniques and systems associated with generating a predicted utilization likelihood for a shared collaboration resource. Integrated resource data associated with a meeting scheduled for a shared collaboration resource is obtained and analyzed using a machine-learned predictive model. The analysis generates a predicted utilization likelihood of the shared resource. An indication of the predicted utilization likelihood is provided to an output system, such as a graphical user interface.

Abstract: When DDoS attack packets are transmitted from the attacker to the victim's server, the attack packets are detected in the edge router of the LAN accommodating the server. These packets are then destroyed, the address of the upstream routers close to the attack source are retrieved, and attack source retrieval modules are transmitted from the edge router to all the upstream routers. By executing the retrieval modules in the upstream routers, verification is performed as to whether the attack packets are passing through those upstream routers. The results are notified to the transmission source router and if the attack packets are passing through, the retrieval modules are transmitted to routers at the upper stream. When the router at the uppermost stream is reached, a protection module is executed to destroy the attack packets. When the attacks cease, the protection module deletes itself and the protection process is ended.

Abstract: When DDoS attack packets are transmitted from the attacker to the victim's server, the attack packets are detected in the edge router of the LAN accommodating the server. These packets are then destroyed, the address of the upstream routers close to the attack source are retrieved, and attack source retrieval modules are transmitted from the edge router to all the upstream routers. By executing the retrieval modules in the upstream routers, verification is performed as to whether the attack packets are passing through those upstream routers. The results are notified to the transmission source router and if the attack packets are passing through, the retrieval modules are transmitted to routers at the upper stream. When the router at the uppermost stream is reached, a protection module is executed to destroy the attack packets. When the attacks cease, the protection module deletes itself and the protection process is ended.