Abstract: An electronic device may determine whether a machine-learning model is operating within predefined limits. In particular, the electronic device may receive, from another electronic device, instructions for the machine-learning model, a reference input and a predetermined output of the machine-learning model for the reference input. Note that the instructions may include an architecture of the machine-learning model, weights associated with the machine-learning model and/or a set of pre-processing transformations for use when executing the machine-learning model on images. In response, the electronic device may configure the machine-learning model based on the instructions. Then, the electronic device may calculate an output of the machine-learning model for the reference input. Next, the electronic device may determine whether the machine-learning model is operating within predefined limits based on the output and the predetermined output.

Abstract: Methods and apparatuses are provided for movie and television series video data analysis. The method includes: gathering and reading, by a processor, a plurality of input movies; removing a video border of each input movie; splitting the input movie into short clips, based on accuracy and efficiency requirements of different analyzing models; assessing attributes of each input movie by analyzing, with the different analyzing models, the input movie, the short clips cut from the input movie, and the frame images extracted from the input movie; and summarizing the plurality of input movies based on matching and integrating the attributes assessed for each input movie.

Abstract: Methods and apparatuses are provided for movie and television series video data analysis. The method includes: gathering and reading, by a processor, a plurality of input movies; removing a video border of each input movie; splitting the input movie into short clips, based on accuracy and efficiency requirements of different analyzing models; assessing attributes of each input movie by analyzing, with the different analyzing models, the input movie, the short clips cut from the input movie, and the frame images extracted from the input movie; and summarizing the plurality of input movies based on matching and integrating the attributes assessed for each input movie.

Abstract: The present disclosure provides a video timing labeling method. The method includes: acquiring a video file to be labeled and text information to be inquired; acquiring a video segment matching the text information to be inquired based on a timing labeling network of a timing labeling model; acquiring a video feature of the video segment matching the text information to be inquired based on a feature extraction network of the timing labeling model; acquiring text information corresponding to the video segment labeled in the video file based on a visual text translation network of the timing labeling model; and outputting the video segment matching the text information to be inquired and the text information corresponding to the video segment labeled in the video file based on the timing labeling model.

Abstract: Methods and apparatuses are provided for editing and generating a short video based upon a long video. The method includes: obtaining a plurality of short source video clips as candidate video clips; obtaining attributes of each short source video clip; obtaining a plurality of target base videos according to a target genre, processing the plurality of target base videos by splitting each target base video into a plurality of short target base video clips, and obtaining attributes of each short target base video clip; selecting short target video clips from the plurality of short source video clips, based on distribution of the attributes obtained for the plurality of the short source video clips and the plurality of short target base video clips; and editing and combining the short target video clips selected from the plurality of short source video clips, to obtain a target video.

Abstract: A style picture generating method, an apparatus and a non-transitory computer readable storage medium thereof are provided. The method includes: obtaining one or more models by training a neural network; obtaining a plurality of interpolated models based on the one or more models; generating a plurality of pictures by the plurality of interpolated models; and generating the style picture by combining two or more pictures in the plurality of pictures using one or more model-specific alpha masks.

Abstract: A method and an apparatus for training a generative adversarial network (GAN) and a method and an apparatus for processing an image are provided. The method for training the GAN includes: obtaining a fine-grained style label (FGSL) associated with the image and inputting the FGSL and a latent vector into a style-based generator in the GAN; the style-based generator generating an first output image based on the FGSL and the latent vector; the projection discriminator determining whether the first output image matches the image based on the FGSL; and adjusting one or more parameters of the GAN and regenerating, by the style-based generator, a second output image based on the FGSL, the latent vector, and the adjusted GAN in response to determining that the first output image does not match the image based on the FGSL.

Abstract: The present disclosure provides a video timing labeling method. The method includes: acquiring a video file to be labeled and text information to be inquired; acquiring a video segment matching the text information to be inquired based on a timing labeling network of a timing labeling model; acquiring a video feature of the video segment matching the text information to be inquired based on a feature extraction network of the timing labeling model; acquiring text information corresponding to the video segment labeled in the video file based on a visual text translation network of the timing labeling model; and outputting the video segment matching the text information to be inquired and the text information corresponding to the video segment labeled in the video file based on the timing labeling model.

Abstract: Systems and methods for instituting redistribution of wireless clients to improve service, comprising via access point (AP), multiple APs acting in concert, and/or a central entity, in connection with a network, allowing a client to associate; deciding that an associated client should be redistributed based on a connection metric, wherein the decision is based on at least one of, a probability calculation, network metric and a threshold comparison; and via the AP, sending the associated client a trigger, wherein the trigger is at least one of, a redistribution request and a disconnect message.

Abstract: Systems and methods for instituting redistribution of wireless clients to improve service, comprising via access point (AP), multiple APs acting in concert, and/or a central entity, in connection with a network, allowing a client to associate; deciding that an associated client should be redistributed based on a connection metric, wherein the decision is based on at least one of, a probability calculation, network metric and a threshold comparison; and via the AP, sending the associated client a trigger, wherein the trigger is at least one of, a redistribution request and a disconnect message.

Abstract: An electronic device is provided that balances the force applied to temperature control elements such that stress within components of the electronic device can be effectively managed. In one example, an electronic device is provided that includes a printed circuit board (PCB), a chip package, a thermal management system, a thermal spreader, and first and second biasing members. The chip package is mounted to the PCB. The thermal management system and spreader are disposed the opposite of the chip package relative to the PCB. The first biasing member is configured to control a first force sandwiching the chip package between the thermal spreader and the PCB. The second biasing member is configured to control a second force applied by the thermal management system against the thermal spreader. The first force can be adjusted separately from the second force so that total forces applied to the chip package and PCB may be effectively balanced.

Abstract: A system for teaching compositionality to convolutional neural networks includes an unmasked convolutional neural network comprising a first set of convolutional neural network layers; a first masked convolutional neural network comprising a second set of convolutional neural network layers; the unmasked convolutional neural network and the first masked convolutional network sharing convolutional neural network weights; the system training the unmasked and first masked convolutional neural networks simultaneously based on an objective function that seeks to reduce both discriminative loss and compositional loss.

Abstract: A system for teaching compositionality to convolutional neural networks includes an unmasked convolutional neural network comprising a first set of convolutional neural network layers; a first masked convolutional neural network comprising a second set of convolutional neural network layers; the unmasked convolutional neural network and the first masked convolutional network sharing convolutional neural network weights; the system training the unmasked and first masked convolutional neural networks simultaneously based on an objective function that seeks to reduce both discriminative loss and compositional loss.

Abstract: An electronic device may determine whether a machine-learning model is operating within predefined limits. In particular, the electronic device may receive, from another electronic device, instructions for the machine-learning model, a reference input and a predetermined output of the machine-learning model for the reference input. Note that the instructions may include an architecture of the machine-learning model, weights associated with the machine-learning model and/or a set of pre-processing transformations for use when executing the machine-learning model on images. In response, the electronic device may configure the machine-learning model based on the instructions. Then, the electronic device may calculate an output of the machine-learning model for the reference input. Next, the electronic device may determine whether the machine-learning model is operating within predefined limits based on the output and the predetermined output.

Abstract: An electronic device may determine whether a machine-learning model is operating within predefined limits. In particular, the electronic device may receive, from another electronic device, instructions for the machine-learning model, a reference input and a predetermined output of the machine-learning model for the reference input. Note that the instructions may include an architecture of the machine-learning model, weights associated with the machine-learning model and/or a set of pre-processing transformations for use when executing the machine-learning model on images. In response, the electronic device may configure the machine-learning model based on the instructions. Then, the electronic device may calculate an output of the machine-learning model for the reference input. Next, the electronic device may determine whether the machine-learning model is operating within predefined limits based on the output and the predetermined output.

Abstract: After analyzing images or videos, a computer system may display or present visual performance feedback with an interactive visual representation of a data-analysis pipeline, where the visual representation includes separate and coupled data-analysis operations in a set of data-analysis operations that includes the one or more machine-learning models. Moreover, in response to a user-interface command the specifies a given data-analysis operation, the computer system may display or present a group of images or videos and associated performance information for the given data-analysis operation, where a given image or video corresponds to an instance of the given data-analysis operation. Furthermore, when the computer system receives user feedback about one at least one of the images or videos in the group of images or videos, the computer system performs a remedial action based at least in part on the user feedback. For example, the computer system may dynamically modify the data-analysis pipeline.

Abstract: Systems and methods for instituting redistribution of wireless clients to improve service, comprising via access point (AP), multiple APs acting in concert, and/or a central entity, in connection with a network, allowing a client to associate; deciding that an associated client should be redistributed based on a connection metric, wherein the decision is based on at least one of, a probability calculation, network metric and a threshold comparison; and via the AP, sending the associated client a trigger, wherein the trigger is at least one of, a redistribution request and a disconnect message.

Abstract: Systems and methods for instituting redistribution of wireless clients to improve service, comprising via access point (AP), multiple APs acting in concert, and/or a central entity, in connection with a network, allowing a client to associate; deciding that an associated client should be redistributed based on a connection metric, wherein the decision is based on at least one of, a probability calculation, network metric and a threshold comparison; and via the AP, sending the associated client a trigger, wherein the trigger is at least one of, a redistribution request and a disconnect message.

Abstract: A computer system may train and use a machine-learning model to quantitatively analyze an image. In particular, the computer system may generate the machine-learning model based on a set of reference images that include content with instances of a quantitative feature attribute and one or more feedback metrics that specify locations of the instances of the quantitative feature attribute in the reference images and numerical values associated with the instances of the quantitative feature attribute. Then, after receiving the image from an electronic device, the computer system may analyze the image using the machine-learning model to perform measurements of one or more additional instances of the quantitative feature attribute in the image. Moreover, the computer system may provide a measurement result for the image, the measurement result including a second numerical value associated with the one or more additional instances of the quantitative feature attribute in the image.

Abstract: A system for teaching compositionality to convolutional neural networks includes an unmasked convolutional neural network comprising a first set of convolutional neural network layers; a first masked convolutional neural network comprising a second set of convolutional neural network layers; the unmasked convolutional neural network and the first masked convolutional network sharing convolutional neural network weights; the system training the unmasked and first masked convolutional neural networks simultaneously based on an objective function that seeks to reduce both discriminative loss and compositional loss.