Abstract: Apparatuses and methods are provided for classifying textual content using a text classifier for determining to which class the textual content belongs. After classification, the text classifier provides the classification result and a context relevant to the classification result to an explanation system. The explanation system predicts, from the classification result and the context relevant to the classification result, one or more reasons behind the classification result.

Abstract: There is provided a client adapted for generating personalized cold-start federated recommendations for a user of the client. The client generates personalized recommendations for three cold-start scenarios, namely i) recommendation of an item to a new user which does not have any history of user-item interactions, ii) recommendation of a new item to a set of the most prospective users where the item has no history, and iii) recommendation of a new item to a new user, where there is no history associated with either the user or the item. The client uses a federated multi-view matrix factorization method to generate cold-start recommendations without transferring users' personal data to a remote server. Further, a server and a content provider for assisting in generating the personalized cold-start recommendations are provided in a federated set-up according to some aspects.

Abstract: A user equipment includes a processor configured to download a master machine learning model for generating a user recommendation related to use of an application of the user equipment, calculate a model update for the master machine learning model using the master machine learning model and data related to one or more of a user of the user equipment or a user interaction with the user equipment, encode the calculated model update using an ?-differential privacy mechanism and transmit the ?-differential privacy encoded model update.

Abstract: A Federated learning server and a method are provided. The Federated learning server is configured to aggregate a plurality of received model updates to update a master machine learning model. Once a pre-defined threshold or interval for received model updates is reached, a set of current hyper-parameter values and corresponding validation set performance metrics obtained from the updated master machine learning model are sent to a hyper-parameter optimization model. The optimization model infers the next set of optimal hyper-parameters using pairwise history of hyper-parameter values and the corresponding performance metrics. The inferred hyper-parameter values are sent to the Federated Learning server which updates the master machine learning model with the updated set of hyper-parameter values and redistributes the updated master machine learning model with the updated set of hyper-parameter values.

Abstract: A client machine is configured for an efficient machine learning process that has access to the client data of all clients connected to a server. The machine learning process is also secure because the client data related to each individual client remains on the same client. Since the server connected to the client does not collect or store large amounts of client data, the machine learning process is time efficient and cost effective.

Abstract: A client including a processor and a memory having computer readable instructions stored thereon that, when executed by the processor, cause the client to connect to a server utilizing a global set of items and at least one model. The client is also caused to utilize at least one model downloaded from said server. The client is additionally caused to generate a recommendation set, including at least one of said items based on said at least one of said downloaded models and a local client data set stored on said client. The recommendation set includes a personalized item recommendation for a user of said client.

Abstract: Embodiments of the present invention may provide a device to adaptively generate a haptic effect. The device may include a controller to generate a haptic command associated with a haptic profile and a haptic driver to generate a drive signal based on the haptic command, wherein the drive signal causes an actuator to produce vibrations corresponding to a haptic effect. Further, the device may include a sensor, coupled mechanically to the actuator, to measure at least one property of the vibrations. The controller may adjust the haptic command according to the measured at least one property. Therefore, the device may continuously tune haptic effect generation according to vibration measurements.

Abstract: Techniques to provide a three-dimensional (“3D”) user interface (“UI”) processing system for a device that may include at least one sensor, a 3D display, and a controller. The controller may include a memory, which may store instructional information, and a processor. The processor may be configured to receive sensor data from the sensor(s) and to interpret sensor data according to the instructional information. The processor may generate a user interface command(s) and transmit the command(s) to the 3D display to control and/or manipulate the 3D display. The processor may also generate host commands to control and/or execute applications within a host system of the device.

Abstract: The present invention may provide a device including a haptic driver to drive a coupled actuator causing the actuator to generate a vibratory haptic effect. A touch screen may display a user interface and may include a sensor to detect user interaction with the touch screen within a predetermined range above the touch screen. A controller may calculate a proximity event based on the detected user interaction above the touch screen, and to control haptic driver operations according to the proximity event.

Abstract: Embodiments of the present invention may provide a device to adaptively generate a haptic effect. The device may include a controller to generate a haptic command associated with a haptic profile and a haptic driver to generate a drive signal based on the haptic command, wherein the drive signal causes an actuator to produce vibrations corresponding to a haptic effect. Further, the device may include a sensor, coupled mechanically to the actuator, to measure at least one property of the vibrations. The controller may adjust the haptic command according to the measured at least one property. Therefore, the device may continuously tune haptic effect generation according to vibration measurements.

Abstract: Embodiments of the present invention provide a user interface processing system for a device that may include at least one sensor, at least one output device, and a controller. The controller may include a memory, which may store instructional information, and a processor. The processor may be configured to receive sensor data from the at least one sensor and to interpret sensor data according to the instructional information. The processor may also generate a user interface feedback command and transmit the command to the at least one output device. Furthermore, the processor may report the sensor data to a host system of the device. By processing the sensor data and generating a corresponding feedback response, for example a haptic response, without the need for host system processing, the user interface controller may decrease latency in providing the feedback response to the user.

Abstract: A computer-implemented method for determining cluster centers in a first data structure, wherein the first data structure comprises a lattice structure of weight vectors that create an approximate representation of a plurality of input data points. The method can include performing a first iterative process for iteratively updating the weight vectors such that they move toward cluster centers; performing a second iterative process for iteratively updating a second data structure utilizing results of the iterative updating of the first data structure; and determining the weight vectors that correspond to cluster centers of the input data points on the basis of the second data structure.

Abstract: A computer-implemented method for determining cluster centres in a first data structure, wherein the first data structure comprises a lattice structure of weight vectors that create an approximate representation of a plurality of input data points. The method comprises performing a first iterative process (81) for iteratively updating the weight vectors such that they move toward cluster centres; performing a second iterative process (82) for iteratively updating a second data structure utilizing results of the iterative updating of the first data structure; and determining the weight vectors that correspond to cluster centres of the input data points on the basis of the second data structure.

Abstract: When optimising the performance of the network, first of all, the relevant key performance indicators for a specific entity within the network as well as first parameters, which influence the key performance indicators, are determined. A number of entities similar to said specific entity is selected, wherein relevant key performance indicators are associated to every entity. The key performance indicators as well as the selected number of entities are used as elements in a first cost function, i.e. said first cost function is calculated on the basis of the KPI and the number of entities. Said first cost function is calculated in order to evaluate the network performance. Accordingly, since said first parameters directly relate to the key performance indicators, the network performance will depend on the values of said first parameters.

Abstract: A flame detector with a radiation sensor (1) sensitive in the ultra-violet and/or visible range of the electro-magnetic spectrum, forms from the signal U1 at the output of the radiation sensor, a first signal U2 which is proportional to the direct voltage portion of the signal U1, and a second signal U3 which is proportional to the alternating voltage portion of the signal U1. The output signal UA of the flame detector is formed such that UA=U2−U3. Ignition sparks can thereby be effectively suppressed.