Abstract: A fake voice detection method based on dual-track differential modeling includes: converting, by a pre-trained single/dual-track voice conversion model, a single-track audio into a stereo; extracting left and right-track Mel spectrogram features; performing texture enhancement on absolute values of differences between an original single-track Mel spectrogram feature and each of the left and right-track Mel spectrogram features; inputting texture enhancement results into left and right dual-branch feature extractors respectively to acquire a final attention map through feature extraction, processing and fusion; and inputting the final attention map into an attention pooling layer and a final binary classification layer to acquire a fake voice detection result.

Abstract: The disclosed systems and methods provide a framework for a proactive prediction of the toxic propensity of an article. Prior to the publication and/or reception of comments to online content, the disclosed framework determines the toxic propensity of the content's context and/or specific words, sentences, sentiments, tone or other messages receivable from consumption of the content. Thus, disclosed framework performs proactive forecasting of the content's toxicity propensity”, which quantifies how likely the content is prone to incur or attract toxic comments. The framework can function and/or be configured to operate in a manner that can perform specifically adherent moderation actions that correspond to the content and control how the content can be interacted with, based on the toxic propensity determination, prior to the content's publication in an effort to thwart, prevent or stop toxic environments surrounding or stemming from the content from coming into existence.

Abstract: The present disclosure provides methods, devices, apparatus, and storage medium for determining a target image region of a target object in a target image. The method includes: obtaining a target image comprising a target object; obtaining an original mask and an image segmentation model, the image segmentation model comprising a first unit model and a second unit model; downsampling the original mask based on a pooling layer in the first unit model to obtain a downsampled mask; extracting region convolution feature information of the target image based on a convolution pooling layer in the second unit model and the downsampled mask; updating the original mask according to the region convolution feature information; and in response to the updated original mask satisfying an error convergence condition, determining a target image region of the target object in the target image according to the updated original mask.

Abstract: A method for determining a target image to be labeled includes: obtaining an original image and an autoencoder (AE) set, the original image being an image having not been labeled, the AE set including N AEs; obtaining an encoded image set corresponding to the original image by using the AE set, the encoded image set including N encoded images, the encoded images being corresponding to the AEs; obtaining the encoded image set and a segmentation result set corresponding to the original image by using an image segmentation network, the image segmentation network including M image segmentation sub-networks, and the segmentation result set including [(N+1)*M] segmentation results; determining labeling uncertainty corresponding to the original image according to the segmentation result set; and determining whether the original image is a target image according to the labeling uncertainty.

Abstract: The present teaching relates to method, system, medium, and implementations for text processing. Upon receiving input data including an original token and a ground truth token label for the original token, a manipulation is applied to the original token to generate a manipulated token based on which to generate manipulated input data. Training data is generated based on the manipulated input data, the ground truth token label, and a ground truth action that, when applied to the manipulated token, yields the original token with the ground truth token label. A text moderation model is trained based on the training data.

Abstract: The present disclosure provides methods, devices, apparatus, and storage medium for determining a target image region of a target object in a target image. The method includes: obtaining a target image comprising a target object; obtaining an original mask and an image segmentation model, the image segmentation model comprising a first unit model and a second unit model; downsampling the original mask based on a pooling layer in the first unit model to obtain a downsampled mask; extracting region convolution feature information of the target image based on a convolution pooling layer in the second unit model and the downsampled mask; updating the original mask according to the region convolution feature information; and in response to the updated original mask satisfying an error convergence condition, determining a target image region of the target object in the target image according to the updated original mask.

Abstract: One or more computing devices, systems, and/or methods are provided. In an example, a first performance metric score may be determined based upon first content item text. A plurality of similarity scores associated with a plurality of sets of content item text may be determined. One or more sets of content item text may be selected from among the plurality of sets of content item text based upon the plurality of similarity scores and a plurality of performance metric scores associated with the plurality of sets of content item text. The plurality of performance metric scores may comprise one or more performance metric scores associated with the one or more sets of content item text. The one or more performance metric scores may be higher than the first performance metric score. One or more representations of the one or more sets of content item text may be displayed.

Abstract: An image segmentation method is provided for a computing device. The method includes obtaining a general tumor image, performing tumor localization on the tumor image to obtain a candidate image indicating a position of a tumor region in the general tumor image, inputting the candidate image to a cascaded segmentation network constructed based on a machine learning model, and performing image segmentation on the general tumor region in the candidate image using a first-level segmentation network and a second-level segmentation network in the cascaded segmentation network to obtain a segmented image.

Abstract: The disclosure relates to methods, devices, systems, and computer storage medium for performing medical image segmentation. The method includes: The method includes: obtaining, by a device, a first medical image and a second medical image with a labeled region; performing, by the device, feature extraction on the first medical image and the second medical image respectively, to obtain first feature information of the first medical image and second feature information of the second medical image; obtaining, by the device, optical flow motion information from the second medical image to the first medical image according to the first feature information and the second feature information; and segmenting, by the device, the first medical image according to the optical flow motion information and the labeled region, to obtain a segmentation result of the first medical image.

Abstract: A computer device obtains a plurality of medical images. The device generates a texture image based on image data of a region of interest in the medical images. The device extracts a local feature from the texture image using a first network model. The device extracts a global feature from the medical images using a second network model. The device fuses the extracted local feature and the extracted global feature to form a fused feature. The device performs image classification based on the fused feature.

Abstract: One or more computing devices, systems, and/or methods are provided. In an example, a first performance metric score may be determined based upon first content item text. A plurality of similarity scores associated with a plurality of sets of content item text may be determined. One or more sets of content item text may be selected from among the plurality of sets of content item text based upon the plurality of similarity scores and a plurality of performance metric scores associated with the plurality of sets of content item text. The plurality of performance metric scores may comprise one or more performance metric scores associated with the one or more sets of content item text. The one or more performance metric scores may be higher than the first performance metric score. One or more representations of the one or more sets of content item text may be displayed.

Abstract: The pure mechanical well deviation wireless measure-while-drilling and mud pulse generation device includes an outer cylinder and an inner cylinder coaxially arranged inside the outer cylinder; an inner flow channel is formed inside the inner cylinder, and an outer flow channel is formed between the inner cylinder and the outer cylinder; a flow control valve and a hydraulic turbine are arranged in the inner flow channel, and the flow control valve is located at an upstream section of the hydraulic turbine. The device further includes a signal generation base and a rotary stopper. An overflow hole is formed on the signal generation base and arranged in the annular outer flow channel, and the rotary stopper can periodically shield the overflow hole. The inclinometer includes the pure mechanical well deviation wireless measure-while-drilling and mud pulse generation device, an inclinometer outer cylinder and an eccentric rotary column.

Abstract: The present teaching relates to method, system, medium, and implementations for predicting user segment. An expert hierarchy is created with an initial expert layer with multiple initial experts and at least one augmented expert layer. Each augmented expert layer has one or more augmented experts that are derived via machine training to augment at least the initial experts. When an input is received by the expert hierarchy, each of the experts, including initial and augmented, generates an expert prediction based on the input.

Abstract: The present teaching relates to method, system, medium, and implementations for integrating heterogeneous experts. A nonlinear integration model is characterized by a plurality of parameters and is configured for combining individual expert predictions to generate an integrated expert prediction Values of the plurality of parameters are learned based on training data as well as outputs from the respective plurality of experts generated based on the training data. When provided an input, individual expert predictions from different experts generated based on a given input are combined, using the nonlinear integration model, to derive an integrated expert prediction in response to the given input.

Abstract: The present teaching relates to method, system, medium, and implementations for integrated targeting. An expert hierarchy is constructed with an initial expert layer with multiple initial experts and one or more augmented expert layers with each augmented expert therein augments, via machine learning, experts at any lower layer of the expert hierarchy. A nonlinear integration model is obtained, via machine learning, for combining expert predictions from experts in the expert hierarch based on an input to generate an integrated expert prediction in response to the input.

Abstract: The pure mechanical well deviation wireless measure-while-drilling and mud pulse generation device includes an outer cylinder and an inner cylinder coaxially arranged inside the outer cylinder; an inner flow channel is formed inside the inner cylinder, and an outer flow channel is formed between the inner cylinder and the outer cylinder; a flow control valve and a hydraulic turbine are arranged in the inner flow channel, and the flow control valve is located at an upstream section of the hydraulic turbine. The device further includes a signal generation base and a rotary stopper. An overflow hole is formed on the signal generation base and arranged in the annular outer flow channel, and the rotary stopper can periodically shield the overflow hole. The inclinometer includes the pure mechanical well deviation wireless measure-while-drilling and mud pulse generation device, an inclinometer outer cylinder and an eccentric rotary column.

Abstract: The disclosure discloses an anti-lesion storage method and storage system for cells, tissues and organs, and relates to the technical field of storage of cells, tissues or organs. The method includes: adjusting a partial pressure of carbon dioxide in a suspension medium containing cells, tissues or organs to be stored to 30-50 mmHg. The inventors found that the regulation of the partial pressure of carbon dioxide in the suspension medium is advantageous for improving anti-lesions of cells, tissues or organs. In particular, the regulation of the partial pressure of carbon dioxide in the suspension medium can prolong the in vivo survival of platelets after transfusion of a platelet concentrate and protect the hemostatic function of platelets whiling effectively alleviating PSLs.

Abstract: The disclosed systems and methods provide a framework for a proactive prediction of the toxic propensity of an article. Prior to the publication and/or reception of comments to online content, the disclosed framework determines the toxic propensity of the content's context and/or specific words, sentences, sentiments, tone or other messages receivable from consumption of the content. Thus, disclosed framework performs proactive forecasting of the content's toxicity propensity”, which quantifies how likely the content is prone to incur or attract toxic comments. The framework can function and/or be configured to operate in a manner that can perform specifically adherent moderation actions that correspond to the content and control how the content can be interacted with, based on the toxic propensity determination, prior to the content's publication in an effort to thwart, prevent or stop toxic environments surrounding or stemming from the content from coming into existence.

Abstract: The present teaching relates to method, system, medium, and implementations for text processing. Upon receiving input data including an original token and a ground truth token label for the original token, a manipulation is applied to the original token to generate a manipulated token based on which to generate manipulated input data. Training data is generated based on the manipulated input data, the ground truth token label, and a ground truth action that, when applied to the manipulated token, yields the original token with the ground truth token label. A text moderation model is trained based on the training data.

Abstract: One or more computing devices, systems, and/or methods are provided. In an example, a first performance metric score may be determined based upon first content item text. A plurality of similarity scores associated with a plurality of sets of content item text may be determined. One or more sets of content item text may be selected from among the plurality of sets of content item text based upon the plurality of similarity scores and a plurality of performance metric scores associated with the plurality of sets of content item text. The plurality of performance metric scores may comprise one or more performance metric scores associated with the one or more sets of content item text. The one or more performance metric scores may be higher than the first performance metric score. One or more representations of the one or more sets of content item text may be displayed.