Abstract: A system enables tool to process remote log files as if the log files were located locally. The system relies on technology, such a file system in user space (FUSE), to add log file search results to the local file system. In some embodiments, the log file search results are associated with virtual files in the local file system. A remote data store system communicates with the local system to provide the search results in response to a search query.

Abstract: An image fusing method includes converting a first image into a first intermediate image, converting a second image into a second intermediate image including a plurality of components, fusing one of the plurality of components of the second intermediate image with the first intermediate image to obtain a fused component, and combining the fused component and other ones of the plurality of components of the second intermediate image to obtain a target image.

Abstract: A tracking method includes receiving a start-tracking instruction, determining a tracking target through thermal tracking according to the start-tracking instruction, and marking the tracking target at a preset position of a display interface.

Abstract: A method of attention-based end-to-end (A-E2E) automatic speech recognition (ASR) training, includes performing cross-entropy training of a model, based on one or more input features of a speech signal, determining a posterior probability vector at a time of a first wrong token among one or more output tokens of the model of which the cross-entropy training is performed, and determining a loss of the first wrong token at the time, based on the determined posterior probability vector. The method further includes determining a total loss of a training set of the model of which the cross-entropy training is performed, based on the determined loss of the first wrong token, and updating the model of which the cross-entropy training is performed, based on the determined total loss of the training set.

Abstract: A control device includes a touchscreen and one or more processors. The touchscreen is configured to display an image captured by an imaging device supported by a movable object or a carrier coupled to the movable object, and receive a user input indicative of selection of a position on the touchscreen to display a selected target of the image and selection of a zoom factor for zooming in or out of the selected target. The one or more processors are configured to generate control data based on information about the user input. The control data includes instructions for the imaging device, the carrier, or the movable object to automatically control an attitude of the imaging device for positioning the selected target at or near the selected position on the touchscreen and a zoom level of the imaging device according to the user selected zoom factor.

Abstract: Methods and apparatuses are provided for performing acoustic to word (A2W) speech recognition training performed by at least one processor. The method includes initializing, by the at least one processor, one or more first layers of a neural network with phone based Connectionist Temporal Classification (CTC), initializing, by the at least one processor, one or more second layers of the neural network with grapheme based CTC, acquiring, by the at least one processor, training data and performing, by the at least one processor, A2W speech recognition training based the initialized one or more first layers and one or more second layers of the neural network using the training data.

Abstract: A method for selecting an optimal language model weight (LMW) used to perform automatic speech recognition, including decoding test audio into a lattice using a language model; analyzing the lattice using a first LMW of a plurality of LMWs to determine a first plurality of best paths; analyzing the lattice using a second LMW of the plurality of LMWs to determine a second plurality of best paths; determining a first best path change rate (BCPR) corresponding to the first LMW based on a number of best path changes between the first plurality of best paths and the second plurality of best paths; and determining the first LMW to be the optimal LMW based on the first BCPR being a lowest BCPR from among a plurality of BCPRs corresponding to the plurality of LMWs.

Abstract: A control device includes a display configured to show an image captured by an imaging device supported by a movable object and one or more processors configured to obtain information about a user input indicative of a target shown within the image, and generate data, based on the information about the user input indicative of the target, to effectuate an automatic control of a zoom level of the imaging device and an attitude of the imaging device relative to the target.

Abstract: An image fusion method includes acquiring a trigger signal for flat-field correction, controlling a first image acquisition device to start a flat-field correction function to perform a flat-field correction process on the first image acquisition device according to the trigger signal for the flat-field correction, and obtaining a fused image in the flat-field correction process according to an infrared image acquired by the first image acquisition device and a visible light image acquired by a second image acquisition device. The first image acquisition device does not output infrared images during the flat-field correction process.

Abstract: A control method for controlling a gimbal system includes controlling a first gimbal of the gimbal system to rotate by a first angle and controlling a second gimbal of the gimbal system to rotate by a second angle, so that a signal input/output range of a first load supported by the first gimbal and a signal input/output range of a second load supported by the second gimbal at least partially overlap.

Abstract: A method of attention-based end-to-end (E2E) automatic speech recognition (ASR) training, includes performing cross-entropy training of a model, based on one or more input features of a speech signal, performing beam searching of the model of which the cross-entropy training is performed, to generate an n-best hypotheses list of output hypotheses, and determining a one-best hypothesis among the generated n-best hypotheses list. The method further includes determining a character-based gradient and a word-based gradient, based on the model of which the cross-entropy training is performed and a loss function in which a distance between a reference sequence and the determined one-best hypothesis is maximized, and performing backpropagation of the determined character-based gradient and the determined word-based gradient to the model, to update the model.

Abstract: An image processing method includes obtaining a first band image and a second band image, performing transparency processing on the first band image to obtain an intermediate image, and superimposing the intermediate image and the second band image to obtain a target image. The present disclosure also provide an image processing device and an unmanned aerial vehicle using the method above.

Abstract: An image processing method includes: obtaining a first band image and a second band image; registering the first band image and the second band image; performing an edge detection on the registered second band image to obtain an edge image; and performing a fusion processing on the registered first band image and the edge image to obtain a target image. The present disclosure also provides an image processing device and a UAV using the above method.

Abstract: The present disclosure provides a target-image acquisition method. The target-image acquisition method includes acquiring a visible-light image and an infrared (IR) image of a target, captured at a same time point by a photographing device; weighting and fusing the visible-light image and the IR image to obtain a fused image; and obtaining an image of the target according to the fused image. The present disclosure also provides a photographing device and an unmanned aerial vehicle (UAV) using the method above.

Abstract: Method and apparatus for automatically predicting lexical sememes using a lexical dictionary, comprising inputting a word, retrieving the word's semantic definition and sememes corresponding to the word from an online dictionary, setting each of the retrieved sememes as a candidate sememe, inputting the word's semantic definition and candidate sememe, and estimating the probability that the candidate sememe can be inferred from the word's semantic definition.

Abstract: An image fusing method includes converting a first image into a first intermediate image, converting a second image into a second intermediate image including a plurality of components, fusing one of the plurality of components of the second intermediate image with the first intermediate image to obtain a fused component, and combining the fused component and other ones of the plurality of components of the second intermediate image to obtain a target image.

Abstract: The present disclosure relates to a method for tracking a maximum temperature point. In the method, a first pair of coordinates of a maximum temperature point in an image sensed by an infrared camera is acquired, where the image is an image captured by the infrared camera. A rotation angle of a gimbal equipped with the infrared camera is then determined according to the first pair of coordinates of the maximum temperature point and a pair of coordinates of a target position in the image. The gimbal is then controlled to rotate according to the determined rotation angle, so as to adjust the maximum temperature point in the image captured by the infrared camera to be located at the target position.

Abstract: An image presentation method includes obtaining a first image captured by a first image sensor and a second image captured by a second image sensor; size-processing the first image according to at least one of a target resolution, an aspect ratio of the first image, or an aspect ratio of the second image to generate a size-processed first image having the target resolution; and generating a presenting image at least by combining the size-processed first image and the second image. The presenting image has a preset resolution that is not less than a sum of the target resolution and a resolution of the second image.

Abstract: A method for selecting an optimal language model weight (LMW) used to perform automatic speech recognition, including decoding test audio into a lattice using a language model; analyzing the lattice using a first LMW of a plurality of LMWs to determine a first plurality of best paths; analyzing the lattice using a second LMW of the plurality of LMWs to determine a second plurality of best paths; determining a first best path change rate (BCPR) corresponding to the first LMW based on a number of best path changes between the first plurality of best paths and the second plurality of best paths; and determining the first LMW to be the optimal LMW based on the first BCPR being a lowest BCPR from among a plurality of BCPRs corresponding to the plurality of LMWs.

Abstract: A method of attention-based end-to-end (A-E2E) automatic speech recognition (ASR) training, includes performing cross-entropy training of a model, based on one or more input features of a speech signal, determining a posterior probability vector at a time of a first wrong token among one or more output tokens of the model of which the cross-entropy training is performed, and determining a loss of the first wrong token at the time, based on the determined posterior probability vector. The method further includes determining a total loss of a training set of the model of which the cross-entropy training is performed, based on the determined loss of the first wrong token, and updating the model of which the cross-entropy training is performed, based on the determined total loss of the training set.