Abstract:
A computer implemented method (100) is disclosed for translating a sensory input to a sensory output for communication between first and second entities, wherein the first entity comprises a user and the second entity comprises a user or a computing system. The method comprises obtaining a first profile specifying a communication capability of the first entity, and a second profile specifying a communication capability of the second entity (110) and obtaining a trained Machine Learning (ML) model operable to map an input sensory communication in accordance with one of the first or second profiles to an output sensory communication in accordance with the other of the first or second profiles (120). The method further comprises receiving an input sensory communication from one of the first or second entities (130), using the ML model to map the input sensory communication to an output sensory communication (140), and providing the output sensory communication to the other of the first or second entities (150).
Abstract:
According to a second aspect, it is provided a method for enabling collaborative machine learning. The method is performed by an agent device. The method comprises the steps of: obtaining local input data; generating read interface parameters based on the local input data using a controller neural net being a first model; generating write interface parameters; transmitting a central reading request to the central device; receiving a central reading from the central device; updating the controller neural net of the agent device based on the central reading; and providing a predictor output of local input data based on the controller neural net and a second model of the agent device, the second model having as an input an output of the controller neural net, wherein the predictor output is obtained from the second model.
Abstract:
A computer implemented method of managing power control in a communication system includes generating a graph representation of interdependencies of components of the communication system, wherein the graph representation includes graph nodes corresponding to the components of the communication system and edges between pairs of graph nodes representing dependency relationships between the pairs of nodes. The method generates edge weights for the edges of the graph representation that correspond to the relative importance of the dependency relationship represented by the edge weight, and generates a policy for managing power control by determining an order for switching the components of the communication system on or off based on the edge weights.
Abstract:
Embodiments disclosed herein relate to methods and apparatus for generating video frames when there is a change in the rate of received video data. In one embodiment there is provided a method of processing video data which comprises generating a video frame using received video data (510), encoding said video frame into a latent vector using an encoder part of a generative model (520), modifying the latent vector (525) and decoding the modified latent vector using a decoder part of the generative model to generate a new video frame (530) in response to determining a reduction in generating the video frames using the received video data (515).
Abstract:
There is provided a method comprising: acquiring (110) sensor data related to an object; using the first learning module, identifying (120) the object based on the acquired sensor data using a first learning module and determining (130) a user associated with the identified object; determining (140) a timestamped location of the object based on at least one of the acquired sensor data and one or more locations of the one or more sensors; performing (150) a first analysis to determine whether the current status of the object contains an anomaly based on one or more predefined rules stored in a knowledge base; performing (160) a second analysis to determine whether the current status of the object contains an anomaly, using a second learning module; and validating (170) whether the current status of the object contains an anomaly based on results of the first analysis and results of the second analysis.
Abstract:
A method performed by a node in a telecommunications network for managing faults comprises obtaining (202) predictions of faults in the telecommunications network and time intervals in which the faults are predicted to occur. The method then comprises determining (204) possible actions that could be performed to address the predicted faults and associated resource usages to perform the possible actions, and selecting (206) actions to perform, from the possible actions, in order to address the predicted faults, based on the predicted time intervals and the determined resource usages.
Abstract:
A method (100) of controlling playout of advertisement content during live video streaming at an end-user terminal comprising steps of: receiving (110) advertisement content from an advertisement server; receiving (112) live streamed video content from a content delivery network and playing out the video content; obtaining (114) at least one of image features and audio features of the video content during playout; calculating (116) a content importance rating of video content to be played out during a prediction time window based on said features; and postponing (118) playout of advertisement content scheduled to be played out during the prediction time window if the calculated content importance rating for the prediction time window is above a threshold value.
Abstract:
A method (100) of controlling playout of advertisement content during video-on-demand video streaming on an end-user client terminal comprising steps of: receiving (110) advertisement content from an advertisement server; receiving (112) video-on-demand, VoD, content from a content delivery network; obtaining (114) network quality metrics between the end-user terminal and the content delivery network; predicting (116) whether a stalling event will occur during playout of VoD content within a prediction time window based on the network quality metrics; and playing out (118) received VoD content and received advertisement content, wherein playout of advertisement content within the prediction time window is dependent on a result of the prediction.
Abstract:
Access control node (200), access device (202A), tethering device (204), and methods therein, for enabling wireless access to a communications network (208). One or more access devices (202) having a wireless connection to the network (208) provide (2:1) relay properties to an access control node (200). When detecting (2:2) that network access is wanted for the tethering device (204), the access control node (200) selects (2:3) an access device (202A) based on the obtained relay properties, to be used for sharing wireless connection with the tethering device (204). The access control node (200) then instructs (2:4) the selected access device (202A) to be available as a relay to the communications network (208) for the tethering device (204) via a wireless link between the access device (202A) and the tethering device (204). The tethering device (204) can then access (2:8) the communications network over the wireless link. By using the relay properties as a basis for selecting the access device (202A), the performance of the wireless network access can be improved and unwanted battery consumption can be avoided. Furthermore, no manual actions are required to achieve the wireless network access.
Abstract:
A method of operating a master node in a vertical federated learning, vFL, system including a plurality of workers for training a split neural network includes receiving layer outputs for a sample period from one or more of the workers for a cut-layer at which the neural network is split between the workers and the master node, and determining whether layer outputs for the cut-layer were not received from one of the workers. In response to determining that layer outputs for the cut-layer were not received from one of the workers, the method includes generating imputed values of the layer outputs that were not received, calculating gradients for neurons in the cut-layer based on the received layer outputs and the imputed layer outputs, splitting the gradients into groups associated with respective ones of the workers, and transmitting the groups of gradients to respective ones of the workers.