Abstract: The present disclosure provides a call method and terminal, and a non-transitory computer-readable storage medium. The call method includes: receiving or initiating, by a first terminal, a call request to establish a call with a second terminal; detecting, by the first terminal, at least one of the following: a current task being executed by the first terminal, whether there is one or more third terminals capable of assisting in the call, and a current task being executed by a third terminal; and selecting whether a display interface of the first terminal displays a call assisting option according to the current task being executed by the first terminal and/or the current task being executed by the third terminal; wherein the call assisting option is used to prompt a user to select to answer the call with the second terminal through the assistance of the third terminal.

Abstract: Provided are an emergency treatment system, an emergency treatment method, and an electronic device. The emergency treatment system includes: an information collection terminal, which is deployed in a rescue vehicle and is used for collecting medical data of a subject to be treated and uploading the medical data to a data lake cluster; the data lake cluster, which is used for performing data access, data processing, data storage and data distribution on the medical data of said object; and a medical business system, which is used for acquiring the medical data of said object from the data lake cluster.

Abstract: Various embodiments describe an apparatus, a method, and a computer program product. An example apparatus includes at least one processor; and at least one memory storing instructions that, when executed by the at least one processor, cause the apparatus at least to perform: encoding an input picture by using a first encoder or first encoding parameters; encoding the input picture by using a second encoder or second encoding parameters; generating a first reconstructed picture based on the encoding of the input picture by using the first encoder or the first encoding parameters; and generating a second reconstructed picture based on the encoding of the input picture by using the second encoder or the second encoding parameters.

Abstract: Various embodiments describe apparatus, method and computer program product. An example apparatus includes at least one processor; and at least one non-transitory memory storing instructions that, when executed by the at least one processor, cause the apparatus at least to perform: receiving a frame to be filtered; receiving semantic information about the frame to be filtered; and filtering the frame based at least on the semantic information.

Abstract: An intelligent interactive tablet and brightness adjustment method. The first region receives touch operation instructions from a user, it is able to reduce the brightness of the first region when the user operates the intelligent interactive tablet at a short distance, so as to facilitate the user to interact more comfortably and improve user's experience. A display screen, a touch component and a driving circuit are included, a first region and a second region are determined in a display region of the display screen, a position of the first region does not overlap that of the second region, the first region includes at least a part of contents in the display region, and the first region is configured to receive the touch operation instruction from the user; and a display brightness of the first region is controlled to be lower than that of the second region.

Abstract: A method includes receiving auxiliary information and/or at least one auxiliary feature, the at least one auxiliary feature being based on the auxiliary information; wherein the auxiliary information comprises information available within a decoder, or the auxiliary information comprises a bitstream output from data encoded using an encoder; receiving decoded data generated using the decoder; and generating filtered data with at least one filter using the auxiliary information and/or the at least one auxiliary feature via applying the filter to the decoded data; wherein the at least one filter comprises a learned filter; wherein the filtered data is configured to be used for at least one machine task performed using a model.

Abstract: The present disclosure provides a navigation method applied to a cloud server. The method includes: determining a global route between a starting point and a destination in response to navigation request information transmitted by a mobile terminal, the navigation request information includes at least information of starting point and destination; transmitting the global route to the mobile terminal to guide the user to drive according to the global route; acquiring a current position of the mobile terminal in response to road abnormality information transmitted by the mobile terminal in the driving process of the user; acquiring a shared route in the cloud server, and generating at least one corrected route from the current position to the destination; the corrected route is different from an original remaining route. The present disclosure also provides a cloud server, a mobile terminal, a navigation system, an electronic device, and a computer-readable medium.

Abstract: Various embodiments provide a method, an apparatus, and computer program product. The method comprising: defining an attention block comprising: a set of initial neural network layers, wherein each layer is caused to process an output of a previous layer, and wherein a first layer processes an input of a dense split attention block; core attention blocks process one or more outputs of the set of initial neural network layers; a concatenation block for concatenating one or more outputs of the core attention blocks and at least one intermediate output of the set of initial neural network layers; one or more final neural network layers process at least the output of the concatenation block; and a summation block caused to sum an output of the final neural network layers and an input to the attention block; and providing an output of the summation block as a final output of the attention block.

Abstract: The present disclosure provides a data sharing method, an on-vehicle device, a cloud server, an internet-of-vehicle system, an electronic apparatus, and a computer-readable medium. The data sharing method is applied to the on-vehicle device. The method includes: determining whether there is a sensing blind region around the on-vehicle device based on road condition information acquired by the on-vehicle device in response to a distance between the on-vehicle device and a key road node smaller than a predetermined distance; if there is the sensing blind region around the on-vehicle device, acquiring road condition information of the sensing blind region acquired by an auxiliary equipment; and performing a driving guidance based on the road condition information acquired by the on-vehicle device and the road condition information of the sensing blind region.

Abstract: An example apparatus includes at least one processor; and at least one non-transitory memory comprising computer program code; wherein 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; learn importance of one or more parameters by using a training dataset; define one or more masks for indicating the importance of the one or more parameters for a model finetuning; share at least one mask of the one or more masks with at least one of an encoder or a decoder; finetune at least one parameter of the one or more parameters based at least on the at least one mask; send or signal one or more weight updates corresponding to the at least one parameter in a bitstream to the decoder.

Abstract: An example method includes: receiving a target frame and one or more reference frames; extracting a first feature map from a first predicted target frame predicted from a first reference frame, and a second feature map from a second predicted frame predicted from a second target frame, wherein the first predicted target frame is a backward predicted target frame and the second predicted target frame is a forward predicted target frame; generating a refined residual feature based at least on the first feature map, the second feature map, and a third feature map extracted from a feature decoder net module or circuit; generating a frame residual based at least on the refined residual feature; and generating an output reconstructed frame based at least on the frame residual and an average frame, wherein the average frame represents an average of the first predicted target frame and the second predicted target frame.

Abstract: Various embodiments provide an apparatus, a method, and a computer program product. The apparatus includes at least one processor; and at least one non-transitory memory including computer program code; wherein 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; train or finetune one or more additional parameters of at least one neural network (NN) or a portion of the at least one NN, wherein the one or more additional parameters comprise one or more scaling parameters; and encode or decode one or more media elements based on the at least one neural network or a portion of the at least one NN comprising the trained or finetuned one or more additional parameters.

Abstract: The embodiments relate to an apparatus and a method for encoding and decoding.

Abstract: An apparatus with a corresponding method and computer program product are provided. The apparatus includes at least one processor; and at least one non-transitory memory including computer program code: wherein 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 the steps (1600) of train or finetune at least one neural network (NN) based at least on a temporal persistence scope; and encode or decode one or more media frames elements based at least on the trained or finetuned at least one neural network. A further apparatus with a corresponding method and computer program product are provided.

Abstract: An information interaction method, a computer-readable storage medium and a communication terminal are disclosed. The information interaction method is applied to a local terminal, and includes: in a process of a video call with a remote terminal, in response to a first interaction instruction, performing split-screen display on a display screen of the local terminal to form at least a first split screen for displaying an interface of the video call and a second split screen for displaying a shopping interface; in response to a first selection instruction, acquiring an image of a commodity corresponding to the first selection instruction, with the first selection instruction being an instruction of selecting the commodity from the shopping interface; and displaying a synthesized image obtained by performing image synthesis on the image of the commodity and at least a part of images in the interface of the video call.

Abstract: A method of recommending an object, an electronic device, and a storage medium are provided. The method includes: determining, in response to receiving target user behavior data of a target user from a target client, a target object according to the target user behavior data and at least one candidate user behavior data corresponding to a plurality of candidate users; and sending the target object to the target client, so as to recommend the target object to the target user, where each of the candidate user behavior data is stored in a predetermined block chain, each of the candidate user behavior data corresponds to at least one of a plurality of block chain nodes contained in a block chain network, and each of the candidate user behavior data indicates a preference of a candidate user for at least one candidate object.

Abstract: Various embodiments provide an apparatus, a method and a computer program product for end-to-end learned predictive coding of media frames. An example apparatus includes at least one processor; and at least one non-transitory memory including computer program code; wherein 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: encode or decode one or more media frames for at least one neural network; wherein an inter-frame codec is applied to at least one media frame of the one or more media frames; and wherein a first decoded reference frame and a second decoded reference frame refer to reference frames for the at least one media frame.

Abstract: An example method includes: receiving an input extended block comprising an input block and input margins, wherein the input block is derived from an image or a video frame, and wherein the input block and the input margins are input to a filter, and wherein the input margins comprise pixels of the image or the video frame; filtering the input extended block to obtain a filtered extended block comprising a filtered block and filtered margins; receiving other blocks, wherein the other blocks are derived from the image or video frame, and wherein at least one pixel of the other blocks overlaps with at least one pixel of the filtered margins of the filtered extended block; and blending the filtered extended block with the other blocks, wherein an operation to blend the filtered extended block with the other blocks is applied to two or more overlapping pixels

Abstract: The present disclosure provides a call method and terminal, and a non-transitory computer-readable storage medium. The call method includes: receiving or initiating, by a first terminal, a call request to establish a call with a second terminal; detecting, by the first terminal, at least one of the following: a current task being executed by the first terminal, whether there is one or more third terminals capable of assisting in the call, and a current task being executed by a third terminal; and selecting whether a display interface of the first terminal displays a call assisting option according to the current task being executed by the first terminal and/or the current task being executed by the third terminal; wherein the call assisting option is used to prompt a user to select to answer the call with the second terminal through the assistance of the third terminal.

Abstract: The present disclosure relates to a human-computer interaction method, apparatus, and system, and a computer-readable storage medium. The human-computer interaction method includes: displaying an eraser icon on a touch display screen according to a first touch operation of a user; adjusting at least one of a direction, a size or a shape of the eraser icon according to a second touch operation of the user, wherein the second touch operation includes at least one of a rotation operation or a deformation operation on the eraser icon; and performing an erase operation on an area to be erased according to the adjusted eraser icon.