Abstract: In one embodiment, a method includes accessing, by a computing device, a natural-language input comprising a description of an avatar and generating, from the natural-language input and by a trained avatar-creation model, a set of avatar-attribute feature vectors. The method further includes determining, by a trained avatar-attribute classifier, one or more avatar attributes from the set of avatar-attribute feature vectors; and generating the avatar based on the determined one or more avatar attributes for presentation on a display of a computing device.

Abstract: Example computer-implemented methods and systems for fairness metric estimation are disclosed. One example method includes, for each user of multiple users, obtaining first data associated with a first collection of items, the first collection of items being recommended to the user by a recommendation model. Second data associated with a second collection of items recommended to the user is obtained, the second collection of items being randomly selected for recommendation to the user. A fairness metric is calculated as a calculated fairness metric and based on the first data and the second data.

Abstract: A deep learning based method for integrating demand forecasting and scheduling of online ride-hailing at a hub is provided. The method includes: S1, performing data processing: performing missing value filling, outlier processing and normalization processing on the historical orders of online ride-hailing and relevant feature data of the urban transportation hub; S2, performing feature screening: primarily screening the relevant features by means of a Pearson correlation test and box plot analysis, calculating an influence degree of data of each feature on the orders of online ride-hailing by using an XGBoost algorithm, and secondarily screening the features; S3, performing model construction: constructing an integrated model for demand forecasting and scheduling decision of online ride-hailing at the urban transportation hub; and S4: performing algorithm design: designing a decision tree and deep learning combination algorithm, and calculating the number of online hailed rides to be scheduled.

Abstract: A deep learning model based method for forecasting online ride-hailing short-term demand is provided. The deep learning model based method includes S1: collecting online ride-hailing demand data in a large transportation hub, and preprocessing original data, to form a data set; S2: performing time series decomposition, specifically, decomposing time series data processed in S1 through a variational modal decomposition (VMD) method, to obtain the certain number of intrinsic mode functions; S3: forecasting a decomposed model by means of a deep learning model Transformer; S4: performing sub-series integration, specifically, accumulating forecast results in S3, to obtain an integrated forecast result; and S5: performing forecast error correction, specifically, correcting a forecast error by using a time series forecast model, that is, an autoregressive integrated moving average model (ARIMA).

Abstract: In one embodiment, a method includes accessing, by a computing device, a natural-language input comprising a description of an avatar and generating, from the natural-language input and by a trained avatar-creation model, a set of avatar-attribute feature vectors. The method further includes determining, by a trained avatar-attribute classifier, one or more avatar attributes from the set of avatar-attribute feature vectors; and generating the avatar based on the determined one or more avatar attributes for presentation on a display of a computing device.

Abstract: A method includes obtaining, using at least one processing device of an electronic device, an identification of multiple three-dimensional (3D) objects of interest. The method also includes generating, using the at least one processing device, multiple intermediate 3D objects based on the 3D objects of interest using optimal transport, where the intermediate 3D objects are generated using interpolation or extrapolation based on shapes of the 3D objects of interest. The method further includes presenting, using the at least one processing device, one or more of the intermediate 3D objects to a user.

Abstract: Provided are a tab coating device. The tab coating device is configured to cover the insulating film on the end face of the tab of the tab coating device. It comprises a sleeve and a shaping piece. The sleeve is configured to be arranged on the outer peripheral surface of the electrode assembly in a sleeving mode. The shaping piece is configured to move along the axial direction of the sleeve and extend into the sleeve, to flatten the part of the insulating film that exceeds the end face of the tab onto the end face of the tab.

Abstract: A computer-implemented method is presented for scanning a computer network. The method includes: a) sending a particular network probe to a network address in a computer network; b) receiving a response to the network probe from the network address; c) appending the response to a set of features forming a feature vector; d) determining a next network probe to conduct at the network address; and e) predicting, by the computer processor, the response from the next network probe using the feature vector and a model, where the model is trained using a machine learning method and outputs a probability that a given network address will respond to a network probe.

Abstract: Embodiments of the disclosure provide an image processing method and apparatus, a computer-readable medium, and an electronic device. The image processing method includes: extracting a feature map of a target image; dividing the feature map into target regions; determining weights of the target regions according to feature vectors of the target regions; and generating a feature vector of the target image according to the weights of the target regions and the feature vectors of the target regions.

Abstract: A computer-implemented method is presented for scanning a computer network. The method includes: a) sending a particular network probe to a network address in a computer network; b) receiving a response to the network probe from the network address; c) appending the response to a set of features forming a feature vector; d) determining a next network probe to conduct at the network address; and e) predicting, by the computer processor, the response from the next network probe using the feature vector and a model, where the model is trained using a machine learning method and outputs a probability that a given network address will respond to a network probe.

Abstract: Embodiments of the disclosure provide an image processing method and apparatus, a computer-readable medium, and an electronic device. The image processing method includes: extracting a feature map of a target image; dividing the feature map into target regions; determining weights of the target regions according to feature vectors of the target regions; and generating a feature vector of the target image according to the weights of the target regions and the feature vectors of the target regions.