Abstract: In implementations of systems for stylizing digital content, a computing device implements a style system to receive input data describing digital content to be stylized based on visual styles of example content included in a digital template. The style system generates embeddings for content entities included in the digital content using a machine learning model. Classified content entities are determined based on the embeddings using the machine learning model. The style system generates an output digital template that includes portions of the digital content having the visual styles of example content included in the digital template based on the classified content entities.

Abstract: The present disclosure provides a method for radio-resource scheduling in a telecommunication network. The method comprises selecting at least one objective associated with a radio-resource scheduling from a plurality of objectives; prioritizing at least one flow from a plurality of flows for the selected at least one objective; identifying at least one state parameter from a plurality of state parameters associated with at least one of an active bearers from a plurality of active bearers; inputting at least one of the plurality of state parameters for the at least one of the active bearers to be scheduled during a current transmission time interval (TTI) to a reinforcement machine learning (ML) network, the reinforcement ML network being configured for a reward in accordance with the selected at least one objective; and receiving, from the reinforcement ML network, a radio resource allocation for each of the active bearers for the current TTI.

Abstract: The present disclosure provides a method for radio-resource scheduling in a telecommunication network. The method comprises selecting at least one objective associated with a radio-resource scheduling from a plurality of objectives; prioritizing at least one flow from a plurality of flows for the selected at least one objective; identifying at least one state parameter from a plurality of state parameters associated with at least one of an active bearers from a plurality of active bearers; inputting at least one of the plurality of state parameters for the at least one of the active bearers to be scheduled during a current transmission time interval (TTI) to a reinforcement machine learning (ML) network, the reinforcement ML network being configured for a reward in accordance with the selected at least one objective; and receiving, from the reinforcement ML network, a radio resource allocation for each of the active bearers for the current TTI.

Abstract: Provided are an electronic device and method for managing the perceived loudness of an input audio signal. The electronic device includes a communication unit configured to communicate with internal units and external devices and receive an audio source signal; a loudness adjustment unit configured to receive the audio source signal from the communication unit and obtain an input audio source signal from the audio source signal; a noise separator configured to separate noise from the input audio source signal; a voice booster configured to boost an output audio signal received from the loudness adjustment unit; a storage unit storing instructions; and at least one processor configured to communicate with the communication unit, the loudness adjustment unit, the noise separator, the voice booster, and the storage unit and control operations thereof by executing the stored instructions.

Abstract: A system and method for determining collaboration strength among users in an enterprise is described. The system identifies a plurality of users of an enterprise. The system accesses user interaction data from one or more applications operating within the enterprise. The user interaction data indicate interactions among the plurality of users using the one or more applications. The system computes collaboration metrics for a user of the plurality of users based on the user interaction data, computes a collaboration strength score based the collaboration metrics for the user, and provides the collaboration strength score to a client device of the user.

Abstract: Provided are an electronic device and method for managing the perceived loudness of an input audio signal. The electronic device includes a communication unit configured to communicate with internal units and external devices and receive an audio source signal; a loudness adjustment unit configured to receive the audio source signal from the communication unit and obtain an input audio source signal from the audio source signal; a noise separator configured to separate noise from the input audio source signal; a voice booster configured to boost an output audio signal received from the loudness adjustment unit; a storage unit storing instructions; and at least one processor configured to communicate with the communication unit, the loudness adjustment unit, the noise separator, the voice booster, and the storage unit and control operations thereof by executing the stored instructions.