Abstract: Systems and methods for optimal split federated learning (O-SFL) in a wireless network, including: receiving, by a federal device in the wireless network, local split points associated with a deep neural network (DNN) model over a time period from at least one client device of a plurality of client devices, wherein the plurality of client devices are connected to an edge device for training the DNN model using split federated learning (SFL); determining, by the federal device, an average of the local split points; determining, by the federal device, a global split point for partitioning the DNN model between the at least one client device and the edge device based on the average of the local split points; and applying, by the federal device, the determined global split point to train the DNN model.

Abstract: An example method for communicating AI data from a sender device to a receiver device in a computing environment may include generating protocol instances of AI data; generating AI metadata description based on AI model information; sending a request comprising the AI metadata description to a receiver device for establishing a session; establishing a session by receiving a response from the receiver device based on the request; sending the AI data associated with the AI metadata description, to the receiver device based on the established session. The receiver device may receive the request and determine the compatibility of the metadata description, generate and send a response, followed by receiving the AI data associated with the AI metadata description.

Abstract: Disclosed is a method and device for efficiently providing an artificial intelligence/machine learning (AI/ML) media service by a user equipment (UE), the method including receiving, from a network server, configuration information including information on a trained configuration AI model for checking a capability of the UE associated with a AI split inferencing between the UE and the network server, performing inferencing for a capability discovery of the UE based on the configuration information, and transmitting, from the network server, a capability metrics of the UE based on the inferencing result.

Abstract: A method and system for harmonizing perceptual, quality of multiple applications in extended reality (XR) environment are provided. The method includes receiving, by an XR device, at least one media stream received from each application of plurality of applications in the XR device, determining a perceptual quality score for at least one media stream received from each application of the plurality of applications, determining at least one candidate application with different media quality from plurality of applications based on determined perceptual quality score for at least one media stream received from each application of the plurality of applications, determining a target perceptual quality score for at least one media stream for at least one candidate application based on the perceptual quality score and network parameters, and harmonizing, at least one media parameter of the at least one media stream received from candidate applications based on the target perceptual quality score.

Abstract: A method of managing, by a server device, synchronization source (SSRC) collisions in mission critical services (MCX) is provided. The method includes receiving a request message for requesting to transmit one or more media streams to one or more receiver devices, from one or more sender devices during a session, determining a unique SSRC identifier for each media stream of the one or more media streams, and transmitting the unique SSRC identifier associated with each media stream of the one or more media streams to the one or more sender devices and the one or more receiver devices.

Abstract: A method for providing, by an electronic device, seamless connectivity in a call is provided. The method comprises transmitting satellite communication capability information and converter capability information to one or more other electronic devices; receiving the satellite communication capability information and the converter capability information from the one or more other electronic devices; establishing a call with the one or more other electronic devices using a non-satellite communication network; and transferring the call from the non-satellite communication network to a satellite communication network using the satellite communication capability information and the converter capability information of the electronic device and of the one or more other electronic devices, based on determining that state of the non-satellite communication network is not sufficient for continuing the call.

Abstract: The disclosure relates to a method and system of selectively deploying an application for facilitating quality-of-experience (QoE) in terms of streaming multimedia content in a networking environment comprising a user-equipment (UE) and a networking node provided with a predictive analysis module. The method comprises: capturing parameters pertaining to UE from at least one of a version of the predictive analysis module with respect to the UE, a current processor occupancy within the UE, a power-level within the UE, network conditions pertaining to the access network etc. One or more of the captured parameters and the observed network conditions is analyzed. Based on analysis, inference is drawn for selecting between the predictive analysis model of the UE and of the networking node for thereby enabling a customized streaming of multimedia content at the UE.

Abstract: A computer-implemented method includes obtaining, from a storage, a video to be enhanced, based on a selection of a user; determining corresponding sets of object scores for a plurality of objects identified in the video, respectively, based on a set of predetermined factors; identifying a primary object and one or more secondary objects in the video, among the plurality of objects, based on the corresponding sets of object scores; and applying at least one visual effect to the primary object and at least one secondary object, from the one or more secondary objects, in at least a portion of the video, for obtaining an enhanced video with the at least one visual effect applied at least to the portion of the video.

Abstract: Provided is a method for adaptively streaming an artificial intelligence (AI) model file, including determining a capability of a first electronic device and a capability of a second electronic device, network information associated with the first and second electronic devices, and AI model information associated with the AI model file; determining to adaptively stream the AI model file based on the determined capabilities and information; pre-processing the AI model file; and adaptively streaming the AI model.

Abstract: A method for suppressing a noise portion(s) from a media event by an electronic device is provided. The method includes receiving a voice signal comprising the noise portion(s) and a voice(s) during the media event. Further, the method includes determining a weightage(s) for the noise portion(s) throughout the media event. Further, the method includes determining a plurality of parameters associated with the electronic device, where the plurality of parameters comprises at least one of a preference(s) of a user of the electronic device or a current context of the electronic device. Further, the method includes suppressing the noise portion(s) in the voice signal based on the weightage(s) and the plurality of parameters associated with the electronic device. Further, the method includes generating a media file, where the media file includes the voice(s) and non-suppressed noise portion(s).

Abstract: Embodiments herein provide methods and systems for saving data while streaming a video. The embodiments include streaming a first fragment of the video at a bit-rate based on network conditions. In an embodiment, the scene content complexity of the first fragment can be analyzed while streaming the first fragment. Based on the screen complexity, the second fragment can be streamed at a bit-rate based on the network conditions and the analyzed screen complexity. In another embodiment, a second fragment can be received at a minimum possible resolution and, thereafter, the scene content complexity of the second fragment can be analyzed while streaming the first fragment. Based on the screen complexity of the second fragment, it can be streamed at a bit-rate based on the network conditions and the analyzed screen complexity. There may be no perceptible difference in streamed quality of the first fragment and the second fragment.

Abstract: The disclosure relates to a method and system of selectively deploying an application for facilitating quality-of-experience (QoE) in terms of streaming multimedia content in a networking environment comprising a user-equipment (UE) and a networking node provided with a predictive analysis module. The method comprises: capturing parameters pertaining to UE from at least one of a version of the predictive analysis module with respect to the UE, a current processor occupancy within the UE, a power-level within the UE, network conditions pertaining to the access network etc. One or more of the captured parameters and the observed network conditions is analyzed. Based on analysis, inference is drawn for selecting between the predictive analysis model of the UE and of the networking node for thereby enabling a customized streaming of multimedia content at the UE.

Abstract: A computer-implemented method includes obtaining, from a storage, a video to be enhanced, based on a selection of a user; determining corresponding sets of object scores for a plurality of objects identified in the video, respectively, based on a set of predetermined factors; identifying a primary object and one or more secondary objects in the video, among the plurality of objects, based on the corresponding sets of object scores; and applying at least one visual effect to the primary object and at least one secondary object, from the one or more secondary objects, in at least a portion of the video, for obtaining an enhanced video with the at least one visual effect applied at least to the portion of the video.

Abstract: Embodiments herein provide methods and systems for saving data while streaming a video. The embodiments include streaming a first fragment of the video at a bit-rate based on network conditions. In an embodiment, the scene content complexity of the first fragment can be analyzed while streaming the first fragment. Based on the screen complexity, the second fragment can be streamed at a bit-rate based on the network conditions and the analyzed screen complexity. In another embodiment, a second fragment can be received at a minimum possible resolution and, thereafter, the scene content complexity of the second fragment can be analyzed while streaming the first fragment. Based on the screen complexity of the second fragment, it can be streamed at a bit-rate based on the network conditions and the analyzed screen complexity. There may be no perceptible difference in streamed quality of the first fragment and the second fragment.

Abstract: A processor including a decode unit to receive a vector indexed load plus arithmetic and/or logical (A/L) operation plus store instruction. The instruction is to indicate a source packed memory indices operand that is to have a plurality of packed memory indices. The instruction is also to indicate a source packed data operand that is to have a plurality of packed data elements. The processor also includes an execution unit coupled with the decode unit. The execution unit, in response to the instruction, is to load a plurality of data elements from memory locations corresponding to the plurality of packed memory indices, perform A/L operations on the plurality of packed data elements of the source packed data operand and the loaded plurality of data elements, and store a plurality of result data elements in the memory locations corresponding to the plurality of packed memory indices.

Abstract: A processor including a decode unit to receive a vector indexed load plus arithmetic and/or logical (A/L) operation plus store instruction. The instruction is to indicate a source packed memory indices operand that is to have a plurality of packed memory indices. The instruction is also to indicate a source packed data operand that is to have a plurality of packed data elements. The processor also includes an execution unit coupled with the decode unit. The execution unit, in response to the instruction, is to load a plurality of data elements from memory locations corresponding to the plurality of packed memory indices, perform A/L operations on the plurality of packed data elements of the source packed data operand and the loaded plurality of data elements, and store a plurality of result data elements in the memory locations corresponding to the plurality of packed memory indices.

Abstract: This invention is a method and system for replacing an entry in a cache memory (replacement policy). The cache is divided into a high-priority class and a low-priority class. Upon a request for information such as data, an instruction, or an address translation, the processor searches the cache. If there is a cache miss, the processor locates the information elsewhere, typically in memory. The found information replaces an existing entry in the cache. The entry selected for replacement (eviction) is chosen from within the low-priority class using a FIFO algorithm. Upon a cache hit, the processor performs a read, write, or execute using or upon the information. If the performed instruction was a “write”, the information is placed into the high-priority class. If the high-priority class is full, an entry within the high-priority class is selected for removal based on a FIFO algorithm, and re-classified into the low-priority class.

Abstract: This invention is a method and system for replacing an entry in a cache memory (replacement policy). The cache is divided into a high-priority class and a low-priority class. Upon a request for information such as data, an instruction, or an address translation, the processor searches the cache. If there is a cache miss, the processor locates the information elsewhere, typically in memory. The found information replaces an existing entry in the cache. The entry selected for replacement (eviction) is chosen from within the low-priority class using a FIFO algorithm. Upon a cache hit, the processor performs a read, write, or execute using or upon the information. If the performed instruction was a “write”, the information is placed into the high-priority class. If the high-priority class is full, an entry within the high-priority class is selected for removal based on a FIFO algorithm, and re-classified into the low-priority class.