Abstract: This specification describes systems, apparatus and methods for deploying Tsetlin machine models on resource-constrained devices. According to a first aspect of this specification, there is described apparatus comprising: one or more sensors; at least one processor; and at least one memory with storing instructions that, when executed by the at least one processor, cause the apparatus at to at least: collect one or more sets of sensor data using the one or more sensors; classify the one or more sets of sensor data using an encoded Tsetlin machine. The encoded Tsetlin machine comprises a compressed representation of a trained Tsetlin machine. The compressed representation is based on a number of exclude decisions of the trained Tsetlin machine being greater than a number of include decisions of the trained Tsetlin machine.

Abstract: Apparatus comprising at least one movable reflective surface configured to reflect electromagnetic waves and at least one actuator coupled with the at least one movable reflective surface, wherein said at least one actuator is configured to at least temporarily drive a movement of said at least one reflective surface.

Abstract: Methods and systems are disclosed for updating learned models. An embodiment comprises receiving a plurality of data sets representing sensed data from one or more devices and determining, using one or more local learned models, local parameters based on the received data sets. Another operation may comprise generating a combined data set by combining the plurality of data sets and, determining, using one or more local learned models, global parameters based on the combined data set. Another operation may comprise transmitting, to a remote system, the global parameters for determining updated global parameters using one or more global learned models based at least partially on the global parameters, and receiving, from the remote system, the updated global parameters. Another operation may comprise updating the one or more local learned models using both the local parameters and updated global parameters.

Abstract: An apparatus comprising: at least one photoplethysmography sensor configured to determine pulse information of a user; at least one audio sensor configured to determine heart sound information of the user; and means for determining, based, at least in part, on the pulse information and the heart sound information, blood pressure information of the user, wherein the at least one photoplethysmography sensor and the at least one audio sensor are comprised in an ear-worn device.

Abstract: An apparatus comprising: multiple cameras including at least a first camera having a first field of view and a second camera having a second field of view, wherein the second camera is different to the first camera and the second field of view is different to the first field of view; and identification means for identifying an object captured by one or more of the multiple cameras, wherein the identification means comprises means for: using visual feature matching for a detected object in the first field of view of the first camera to identify the detected object in the first field of view of the first camera as a first object; and using an expected location of the first object in the second field of view of the second camera to identify a detected object in the second field of view as the first object.

Abstract: An apparatus, method and computer program is described comprising: obtaining local data comprising one or more samples at a user device; computing representations of at least some of said samples by passing said one or more samples through a local feature extractor; clustering the computed representations to generate local centroids; providing generated local centroids and parameters of the local feature extractor to a server; receiving global centroids and global feature extractor parameters from said server; updating the parameters of the local feature extractor based on the received global feature extractor parameters; assigning selected samples of one or more samples and one or more augmentations of said selected samples to global clusters; and further updating the updated parameters of the local feature extractor using machine learning principles, thereby generating a trained local feature extractor.

Abstract: Example embodiments may relate to an apparatus, method and/or computer program for the updating, or tuning, of classifiers. For example, the method may comprise receiving data indicative of a positive or negative classification based on comparing an output value, generated by a computational model responsive to an input data, with a threshold value which divides a range of output values of the computational model into positive and negative classes of output values. A positive or a negative classification may be usable by the apparatus, or another apparatus, to trigger one or more processing operations. Other operations may comprise determining that the positive or negative classification is a false classification based on one or more events detected subsequent to generation of the output value and updating the threshold value responsive to determining that the positive or negative classification is a false classification.

Abstract: Providing unlabelled training data for training a computational model comprises: obtaining sets of time-aligned unlabelled data, wherein the sets correspond to different ones of a plurality of sensors; marking a first sample, of a first set of the sets, as a positive sample, in dependence on statistical separation information indicating a first statistical similarity of at least a portion of the first set to the at least a portion of the reference set and in dependence on the first sample being time-aligned relative to a reference time; marking a second sample, of a second set of the sets, as a negative sample, in dependence on statistical separation information indicating a second, lower statistical similarity, of at least a portion of the second set to the at least a portion of the reference set, and in dependence on the second sample being time-misaligned relative to the reference time.

Abstract: Adapting Models for Artificial Intelligence An apparatus and method is disclosed, the apparatus comprising means for providing a first machine learning model for classifying first input data to one of a first number of classes, for receiving an input indicative of one or more new classes to add to the first machine learning model and for receiving second input data for allocating to the or each new class. The means may be configured to adapt the first machine learning model to provide a second machine learning model by adding the one or more new classes to the first number of classes and to train the second machine learning model using the first input data and the second input data.

Abstract: A method is provided that includes determining a quality of a data portion of an input sensor data stream based, at least in part, on data of a first data type and determining between, at least, generation of two or more streams of a second, different data type including at least one synthesised data stream of the second data type. Determining between generation of two or more streams of a second, different data type is based, at least in part, on the determined quality. The synthesis is based, at least in part, on the data of the first data type. The method further includes causing generation of at least one stream of the second, different data type based, at least in part, on the determination between generation of two or more streams of the second, different data type.

Abstract: In one embodiment, the method includes obtaining, by a first processing device, energy demand data representative of the energy consumption of respective tasks of a processing pipeline, obtaining, by the first processing device, battery availability data representative of the available energy of the batteries of other respective processing devices, for respective tasks of the processing pipeline, selecting, by the first processing device, one of the processing devices for executing the task, as a function of the energy demand data and the battery availability data, and controlling, by the first processing device, the execution of the respective tasks on the selected processing devices.

Abstract: This relates to the use of sensor evaluation in a multi-sensor environment. In a first aspect, this specification describes apparatus comprising: at least one processor; and at least one memory including computer program code. The at least one memory and the computer program code are configured to, with the at least one processor, cause the apparatus at least to perform: receive sensor data from a plurality of sensors collected during a first time period; process the received sensor data through a plurality of layers of a neural network to generate an output indicative of the sensing quality of each of the plurality of sensors for a task; and cause a subset of the plurality of sensors to collect data during a second time period based on the output indicative of the suitability of each of the plurality of sensors for the task.

Abstract: An apparatus comprising means for: using a generative neural network, trained to translate first sensor data to simulated second sensor data, to translate input first sensor data from a first sensor to simulated second sensor data; and providing the simulated second sensor data to a different, specific-task, machine-learning model for processing at least second sensor data.

Abstract: An apparatus comprising means for: receiving information from at least one inertial measurement unit configured to be worn on a user's head; and causing, at least in part, determining facial expression information in dependence on at least the received information.

Abstract: Apparatus comprising at least one movable reflective surface configured to reflect electromagnetic waves and at least one actuator coupled with the at least one movable reflective surface, wherein said at least one actuator is configured to at least temporarily drive a movement of said at least one reflective surface.

Abstract: In one embodiment, the method includes obtaining, by a first processing device, energy demand data representative of the energy consumption of respective tasks of a processing pipeline, obtaining, by the first processing device, battery availability data representative of the available energy of the batteries of other respective processing devices, for respective tasks of the processing pipeline, selecting, by the first processing device, one of the processing devices for executing the task, as a function of the energy demand data and the battery availability data, and controlling, by the first processing device, the execution of the respective tasks on the selected processing devices.

Abstract: An apparatus, method and computer program is described comprising: determining an initial performance of a first model, wherein determining the initial performance comprises deploying the first model at a first device; determining one or more operations for modifying the first model based on at least the initial performance of the first model and one or more user requirements; modifying the first model by performing the one or more operations; determining whether a performance of the modified first model satisfies the one or more user requirements, wherein the determining comprises deploying the modified first model at the first device; and in the event that the modified first model does not satisfy the one or more user requirements, further modifying the first model by performing one or more further operations until the performance of the modified first model satisfies the one or more user requirements, wherein the determining further one or more operations based on at least the performance of the modified f

Abstract: Apparatus comprising at least one reflective surface configured to reflect electromagnetic waves, wherein a reflective response of at least one portion of said reflective surface with respect to said electromagnetic waves is electronically controllable, wherein said apparatus is configured to at least temporarily control said reflective response of said at least one portion of said reflective surface.

Abstract: Methods and systems are disclosed for updating learned models. An embodiment comprises receiving a plurality of data sets representing sensed data from one or more devices and determining, using one or more local learned models, local parameters based on the received data sets. Another operation may comprise generating a combined data set by combining the plurality of data sets and, determining, using one or more local learned models, global parameters based on the combined data set. Another operation may comprise transmitting, to a remote system, the global parameters for determining updated global parameters using one or more global learned models based at least partially on the global parameters, and receiving, from the remote system, the updated global parameters. Another operation may comprise updating the one or more local learned models using both the local parameters and updated global parameters.

Abstract: An apparatus comprising means for: using a generative neural network, trained to translate first sensor data to simulated second sensor data, to translate input first sensor data from a first sensor to simulated second sensor data; and providing the simulated second sensor data to a different, specific-task, machine-learning model for processing at least second sensor data.