31 results about "Relevance prediction" patented technology

Embodiments of the present disclosure provide systems and methods for emotion-based call summarization. One method may include receiving an emotion prediction vector for an utterance text segment from a transcript data object, the emotion prediction vector comprising a plurality of emotion prediction scores respectively corresponding to a plurality of emotion identifiers; generating a domain-specific relevancy prediction for the utterance text segment based on a category-relevant subset of the plurality of emotion prediction scores that correspond to one or more category-specific emotion identifiers of the plurality of emotion identifiers associated with a domain-specific summarization category; identifying the utterance text segment as a relevant utterance from the transcript data object based on a comparison between the domain-specific relevancy prediction and a relevancy threshold; and initiating a performance of a machine learning summarization operation based on the utterance text segment.

The application discloses a multi-agent collaborative perception feature enhancement method based on context aggregation, relates to the technical field of automatic driving and intelligent networked vehicles, and comprises the following steps: acquiring current frame features and at least one frame of historical features; adaptively aligning the historical features based on a motion prediction network and a deformable convolution offset; generating semantic consistency weights and identifying time sequence discontinuous regions based on a correlation prediction network; fusing the current and historical features according to the weights and carrying out state space selective scanning, pooling, denoising and aggregation; generating multi-scale features, combining scene complexity, quality evaluation and time sequence consistency constraints to determine scale weights and then fusing; and inputting an LSTM to perform time sequence modeling and output enhanced context features. Invalid alignment is avoided through motion compensation and semantic consistency constraints, noise and scale jitter are suppressed through position-level denoising and multi-scale adaptive smoothing, and the collaborative perception robustness and detection accuracy are improved.

The disclosure provides a relevance prediction model training method and device, and an abstract generation method and device. The relevance prediction model training method comprises: extracting a first semantic feature vector of a first sentence sample and a second semantic feature vector of a second sentence sample; generating a training sample, wherein the training sample comprises the first semantic feature vector and the second semantic feature vector, and a preset semantic relevance label of the first sentence sample and the second sentence sample; inputting the training sample into a machine learning model to obtain a semantic relevance prediction result of the first sentence sample and the second sentence sample; determining a loss function according to the semantic relevance label and the semantic relevance prediction result; and training the machine learning model by using the loss function to obtain a relevance prediction model.

A device receives comment information that is associated with users and includes comments provided by the users, about an entity, via social media sources, and receives transaction information that is associated with the users and includes financial transactions of the users with the entity. The device determines correlations between the comment information and the transaction information, where the correlations between the comment information and the transaction information provide weights to the comment information to generate weighted comment information. The device generates a prediction about a future stock price of the entity based on the weighted comment information, the transaction information, and the correlations between the comment information and the transaction information, and provides the prediction about the future stock price of the entity for display.

The invention discloses a model training method, a knowledge retrieval method, electronic equipment and a storage medium. The model training method comprises the following steps: inputting a first user question of a user, first context information of the first user question and a first knowledge entry into a first model to obtain predicted correlation between the first user question and the first knowledge entry; wherein the first context information is obtained from historical dialogues with the user, and after the first model encodes the first user question, the first context information and the first knowledge item, a coding vector of the first user question and a coding vector of the first context information are fused to obtain a first fusion vector; performing correlation prediction based on the first fusion vector and the coding vector of the first knowledge entry to obtain predicted correlation; parameters of the first model are adjusted based on the predicted correlation and a reference correlation between the first user question and the first knowledge entry.

This invention belongs to the field of image segmentation technology and discloses a method, system, medium, device, and terminal for extracting identification documents. The system utilizes an embedded device to acquire multispectral images of the identification document; it models the document image edge segments based on their straight-line geometric properties and infers from the contextual information of the global image; it extracts global image features using a ResNet network and then encodes them using a Deformable DETR encoder; in the first-stage decoding process, it predicts the document edge segments using an attention mechanism and learnable line segment and position queries; in the second-stage decoding process, it compares the correlation between line segment features and image features to predict the relative order between line segments, and obtains the complete edges, vertices, and bounding boxes of the document image through perspective transformation. This invention uses a two-part matching method to predict line segments, avoiding pre- and post-processing, simplifying the detection channel, and achieving true end-to-end processing.

The application provides a point cloud encoding method based on a multi-level spherical octree and a graph-driven attention entropy model, comprising: encoding point cloud data using an octree entropy model based on multi-level spherical coordinates; constructing an adjacency matrix of a parent graph and a distance graph; using a graph convolution network module to embed context information with the aid of the adjacency matrix; constructing a grouping graph attention module and a cross attention module to learn the relevance of parent node context and sibling node context; predicting the probability of each octree node placeholder symbol; compressing the placeholder symbol sequence into a binary floating point number sequence and converting it into a bit stream; converting the bit stream into an octree placeholder symbol sequence, reconstructing the octree and restoring the point cloud. The application combines multi-level spherical coordinate octree structure, graph convolution, grouping graph attention module and cross attention module, reduces quantization error, improves compression efficiency, balances calculation complexity and compression effect, and significantly enhances the adaptability to high-resolution point cloud data.

The present application provides a kind of shale oil horizontal well volume fracturing ultimate recoverable reserves prediction method, first based on the reservoir geological parameters of the horizontal well to be measured and adjacent horizontal well in the same block, establish productivity prediction model, calculate effective fracture network sweep efficiency using reservoir numerical simulation method, and establish effective fracture network sweep efficiency and maximum recoverable reserves correlation prediction chart;Secondly, the correlation coefficient between the geology and volume fracturing reconstruction parameters of the horizontal well to be measured and the fracture network sweep volume is calculated using grey relational analysis method, and the key control parameters affecting fracture network sweep volume are determined;Finally, the fracture network sweep volume prediction model coupled with key control parameters is established, the fracture network sweep volume of the horizontal well to be measured is obtained, the effective fracture network sweep efficiency is obtained, and the maximum recoverable reserves of the horizontal well to be measured is obtained using the correlation prediction chart. The method can quickly predict the maximum recoverable reserves of any horizontal well by establishing the correlation chart of effective fracture network sweep efficiency and maximum recoverable reserves.

The present disclosure relates to field of wireless communication network that discloses method and system for configuring initial congestion window value in User Equipment (UE (201)). UE (201) determines for each of one or more applications running in UE (201), user experience parameters and UE (201) network parameters. Further, UE (201) predicts using trained Artificial Intelligence (AI) model (203), an optimal initial congestion window value for each of one or more applications based on correlation of user experience parameters and UE (201) network parameters corresponding to each of one or more applications. Finally, UE (201) configures optimal initial congestion window value for each of one or more applications based on prediction. The present disclosure helps in reducing Flow Completion Time (FCT) of each application by predicting optimal initial congestion window value dynamically.

Dynamic conversation alerts are provided within a communication session. In one embodiment, the system presents, to a client device associated with a user of a communication platform, a user interface (“UI”) including a prompt for the user to submit one or more alert phrases, each alert phrase being associated with a category; receives, from the client device, a list of submitted alert phrases; and receives a transcript of a communication session between participants. For each utterance in the transcript, the system determines whether one or more predictions of relatedness are present between the utterance and one or more alert phrases from the list of submitted alert phrases. The system then transmits, to the client device, a list of related categories, each related category including one or more timestamps of utterances for which a prediction of relatedness is present for an alert phrase associated with that category.

The application belongs to the technical field of audio processing, and a plurality of pre-trained audio text expert models are introduced to construct a multi-expert audio text semantic representation. Meanwhile, the consistency relationship between audio and text at the overall semantic level and the semantic inconsistency between audio and text that is perceptually significant to humans are modeled through a semantic alignment branch and a semantic mismatch branch respectively, and a multi-branch correlation score fusion mechanism is used to output an audio text correlation prediction result that is highly consistent with human subjective evaluation. This method can simultaneously consider semantic consistency and perceptual differences, effectively achieving more accurate and more human subjective evaluation standard-compliant audio text correlation evaluation. Meanwhile, based on the above method, the application constructs an audio retrieval system that evaluates and sorts the correlation between a text query and audio samples in an audio library to realize an audio retrieval function oriented to natural language description.

The application realizes a stream music recommendation method for removing attention deviation based on reward correction through the method in the network security field. The core of the method contains three modules: a reward correction model, an attention prediction module and a correlation prediction module. The reward correction module obtains the unbiased reward after correction by combining the predicted user attention and correlation through the importance sampling method; the attention prediction module models the probability of user attention to each song; the correlation prediction module is used to predict the preference of the user to each candidate song, and the parameter is updated based on the corrected reward. The application eliminates the attention deviation in the user feedback to obtain unbiased reward and improve the prediction accuracy of the model.

This application discloses a method, apparatus, medium, device, and program product for processing pre-training data, relating to the field of artificial intelligence technology. The method includes: acquiring an initial dataset for a target knowledge domain, the initial dataset comprising multiple pre-training data retrieved for the target knowledge domain; performing data quality prediction on the pre-training data in the initial dataset based on a first evaluation model to obtain a quality evaluation result; performing domain relevance prediction on the pre-training data in the initial dataset based on a second evaluation model to obtain a domain relevance prediction result, the relevance prediction result indicating the relevance between the pre-training data and the target knowledge domain, the first and second evaluation models being constructed based on a large-scale language model; filtering the initial dataset based on the quality evaluation result and the relevance prediction result to obtain an intermediate dataset; and performing fine-tuning and filtering on the intermediate dataset to obtain a target pre-training set. This application can efficiently acquire a large amount of high-quality pre-training data.

This disclosure relates to a training method for a video recognition model, a video recognition method, and related equipment. The method includes: acquiring a video sample set, wherein the video samples in the video sample set include display data, audio data, and correlation annotation data between the display data and the audio data; extracting display text data from the display data; performing speech recognition on the audio data to obtain playback text data; inputting the display text data and the playback text data into a first network structure of the video recognition model to obtain a text relation vector; inputting the text relation vector into a second network structure of the video recognition model to obtain correlation prediction data; and training the network parameters corresponding to the second network structure based on the target loss determined by the correlation prediction data and the correlation annotation data to obtain a video recognition model that meets preset conditions. The obtained trained video recognition model can quickly and accurately recognize videos.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for predicting a return to a site. In some implementations, a system obtains data indicative of a time evolving movement of a user interacting with a website shown on the client device. The system determines, using a first trained machine learning model and based on the data indicative of the time evolving movement, a metric associated with an emotion of the user corresponding to the user's interaction with the website. The system obtains, from a metric database, metrics associated with an identifier of the user. The system provides, to a second trained machine learning model, (i) the metric associated with the emotion and (ii) data representing the obtained metrics associated with the identifier. The system generates, using the second trained machine learning model, a prediction indicating whether the user is likely to return to the website.

This invention discloses a method for predicting low-speed turbulent boundary layer noise considering the correlation of flow space, belonging to the field of near-wall noise environment for aircraft. This invention selects key flow field parameters for predicting low-speed turbulent boundary layer noise by analyzing the correlation characteristics of flow space; and obtains ωδ* / U for predicting the power spectrum of turbulent boundary layer noise in different segments. ∞ The values ​​were calculated, and a prediction model for the turbulent boundary layer noise power spectrum was constructed to predict the turbulent boundary layer noise power spectrum; the convection velocity U was analyzed and obtained. c This invention is a key parameter determining the spatial correlation of turbulent boundary layer noise, and it constructs a spatial correlation prediction model for turbulent boundary layer noise. By incorporating the convection velocity into the prediction model, the convection velocity of the turbulent boundary layer noise is predicted, thus obtaining the spatial phase distribution of the noise. This invention can simulate the time-frequency characteristics of noise and accurately reproduce the spatial correlation characteristics of low-speed turbulent boundary layer noise, improving the prediction accuracy of turbulent boundary layer noise.

This invention discloses a commonsense question answering method and system based on dynamic global semantic fusion. The method involves obtaining an initial representation of the question context for the commonsense question to be answered; predicting a soft mask based on the correlation between the initial representation of the question context and the entity representation of the initial knowledge subgraph; generating a dynamic knowledge subgraph based on the soft mask; extracting features from the dynamic knowledge subgraph to obtain its initial representation; fusing word-level local information between the question context and the initial representation of the dynamic knowledge subgraph to obtain a word-level local information fused representation of the question context and the dynamic knowledge subgraph; performing multi-head attention learning on the word-level local information fused representation of the question context and the dynamic knowledge subgraph to obtain an updated representation of the question context and the dynamic knowledge subgraph; pooling the updated representation of the dynamic knowledge subgraph to obtain a pooled representation of the dynamic knowledge subgraph; and obtaining the answer to the commonsense question based on the pooled representation of the dynamic knowledge subgraph.

A prediction method includes a calculation process and a prediction process. The calculation process calculates a correlation between a spatial distribution value of a magnetic field in a chamber when a plasma etching process is performed on a substrate disposed in the chamber, and a process result of the plasma etching process on the substrate. The prediction process predicts the process result of the plasma etching process on the substrate from the spatial distribution value of the magnetic field in the chamber based on the calculated correlation.

The application discloses a navigation route processing method and device, a storage medium and an electronic device, and can be applied to the field of maps. The method comprises the following steps: determining a group of navigation routes and a group of parking lots between a navigation starting point and a navigation ending point, obtaining a group of parking paths, each parking path comprising a combination of a navigation route and a parking lot; determining a vacant parking space feature for describing a historical vacant parking space of each parking lot in a target period, the target period being a period from the navigation starting point to the navigation ending point; determining a traffic flow state feature for describing a historical traffic flow state of each navigation route corresponding to the target period; inputting the vacant parking space feature of each parking lot and the traffic flow state feature of each navigation route into a target prediction model to obtain a target parking path, the target prediction model being used for predicting a selection probability of each parking path based on the correlation between the vacant parking space feature of each parking lot and the traffic flow state feature of each navigation route.

The application discloses a trajectory planning method and device, equipment, vehicle, medium and product. The method comprises the following steps: acquiring first environment information of an environment where a target vehicle is located, the first environment information being used for describing a plurality of first traffic participants in the environment where the target vehicle is located; predicting, by a pre-acquired task correlation predictor, based on the first environment information, to obtain a task correlation of each first traffic participant, the task correlation of the first traffic participant being used for representing an influence degree of the first traffic participant on a current driving behavior of the target vehicle; screening the plurality of first traffic participants according to the task correlation of each first traffic participant to obtain a target traffic participant; and performing trajectory planning on the target vehicle according to the target traffic participant to obtain a target trajectory. The first traffic participant with a larger task correlation can be screened out to participate in subsequent trajectory planning on the target vehicle, so that attention to redundant information is reduced, and the accuracy of trajectory planning is improved.

The present disclosure relates to a text processing method, a model training method, a device, an apparatus and a storage medium. The text processing method comprises: comparing text content in a to-be-labeled document with text content in each initial label respectively, and determining a candidate labeling label from each initial label according to a comparison result; inputting the to-be-labeled document and the candidate labeling label into a relevance prediction model to perform relevance prediction, to obtain an initial relevance parameter of the to-be-labeled document and the candidate labeling label; obtaining, from a preset database, a supplementary document feature corresponding to the to-be-labeled document and a supplementary label feature corresponding to the candidate labeling label; adjusting the initial relevance parameter by using a relevance adjustment model through the supplementary document feature and the supplementary label feature to obtain a target relevance parameter; and determining a target labeling label from the candidate labeling label based on the target relevance parameter. In this way, the accuracy of determining the target labeling label can be improved.

This invention discloses a method for locating generator units based on low-frequency oscillation mode correlation in a power system. It constructs a generator unit location model guided by modal information and combines it with a phased training strategy to achieve coordinated optimization of low-frequency oscillation mode identification and related generator unit location. Specifically, in the first phase, the first branch of the model is trained using generator operation measurement data and power system topology information. In the second phase, the second branch is trained based on the low-frequency oscillation mode parameters output from the first phase and the generator power angle time series. During generator unit location, the two types of features are fused through feature mapping and weight allocation mechanisms, allowing oscillation mode information to participate in the weight calculation of generator time series features, thereby obtaining the correlation prediction results of each generator under different oscillation modes.

The application provides a virtual camera behavior generation method and system, which fuses global performance scene state features and global camera behavior trend features into performance scene global multi-data fusion features, and pre-constructs a camera behavior trend coding module based on the global camera behavior trend features and a camera behavior generation decoding module based on the global performance scene state features; fuses a performance scene multi-data loss function to construct a camera behavior generation model based on the virtual performance global multi-data fusion features to predict a camera behavior sequence, and transmits the camera behavior sequence into a performance scene visualized interactive interface based on a Unity editor to display the generated camera behavior trajectory. The application fully utilizes the global correlation of the camera behavior trend and the strong correlation between the camera behavior trend and the global state of the performance scene to predict the performance scene camera behavior trajectory, and has the advantage of high camera behavior prediction accuracy.

This system provides an autonomous driving system that can appropriately plan the vehicle's behavior while reducing the overall computational load by accurately predicting the future paths of moving objects that indirectly affect the vehicle's driving plan. [Solution] An autonomous driving system that predicts the future path of a moving object around its own vehicle, comprising: an environment recognition unit that recognizes the environment around the vehicle based on the output of a sensor; a simple prediction unit that predicts the future path of a target object using a simple AI model; an advanced prediction unit that predicts the future path of a target object using an advanced AI model; an object correlation acquisition unit that acquires the correlation between the target object and an object other than the vehicle; a prediction method selection unit that selects the simple prediction unit or the advanced prediction unit based on the correlation; and a vehicle control unit that controls the vehicle taking into account the future path of the target object predicted by the simple prediction unit or the advanced prediction unit.

This application discloses a semantic relevance prediction model, method, apparatus, storage medium, and computer device, relating to the field of Internet technology. The model includes a student model and an online prompting model. The student model comprises: a parameter-sharing dual-tower network for extracting features from the first and second keywords to be predicted, obtaining first keyword features and second keyword features; a thought chain tower network for extracting features from the thought chain prompts generated by the online prompting model, obtaining thought chain features; the thought chain tower network and the dual-tower network do not share parameters; an expert hybrid network for fusing the first keyword features, second keyword features, and thought chain features, obtaining keyword fusion features; and a deep neural network for performing semantic relevance prediction based on the keyword fusion features, obtaining the semantic relevance prediction result between the first and second keywords. The above model can improve the accuracy of relevance prediction.

Automated content recommendation is used in conferencing. In one embodiment, a system receives a list of content recommendation actions. The system receives a number of utterances associated with the participants in real time. For each utterance, the system determines whether a prediction of relatedness is present between the utterance and one or more trigger phrases associated with a content recommendation action. Upon determining that a prediction of relatedness is present, the system performs the associated content recommendation action by transmitting, to one or more client devices, one or more pieces of content to be recommended.

The application discloses an intention recognition method, which comprises the following steps: obtaining a question and answer correlation label by performing correlation prediction on historical dialogue data through a preset training model; obtaining an intention recognition model by iteratively training the preset training model through a historical question, a historical answer and the question and answer correlation label corresponding to the same historical dialogue data; obtaining a target intention recognition model by fine-tuning the intention recognition model through a target scene question, a target scene answer and a target scene label; and performing intention recognition on a to-be-recognized question through the target intention recognition model to obtain an intention recognition result. The preset training model is pre-trained through a large amount of unlabeled historical dialogue data, and can be applied to a target scene through target scene data fine-tuning, so that the intention recognition model has strong generalization. The intention recognition accuracy of the target intention recognition model is improved through the target intention recognition model.

Systems and methods for predicting relevance of resources to search queries. In some aspects, the system may receive a search query requesting resources from a database and may identify resources, including messages, relating to the search query. The system may extract, from the messages, hyperlinks specifying locations within the database. The system may input, into a model, the search query and the hyperlinks to cause the model to generate predictions of relevance of the hyperlinks to the search query. The system may then determine an overall relevance score for the resources in relation to the search query based on the predictions of relevance.

Provided are an information processing method, an information processing device, and a computer program.　The present invention acquires, for each of a plurality of conditions obtained by changing one or more setting values in a plasma treatment recipe, time series data measured by a specific sensor when plasma treatment is executed, calculates, for each of the plurality of conditions, the average value of the time series data in a specific step section of the plasma treatment recipe, derives a correlation between the setting value and the calculated average value, compares, for each of the plurality of conditions, the calculated average value and a predicted value predicted from the correlation, and determines the stability of the plasma treatment recipe on the basis of the comparison result between the average value and the predicted value.

The application discloses a CLATT stock correlation prediction method based on Bayesian optimization, comprising the following steps: constructing a database, including stock data and corresponding factor data in at least a period of time; preprocessing the data in the database, calculating the correlation matrix between different factors, and eliminating strongly correlated factors; decomposing the stock yield data based on a multi-factor model, calculating the corresponding factor values, and calculating the correlation data between stocks by using a Pearson correlation coefficient; fusing and splicing the stock yield data and the correlation data to obtain an input vector, so as to enhance the multi-source nature of the input data, improve the prediction accuracy, input the input vector into a prediction model, and output a prediction result, which has higher accuracy and robustness and is suitable for various stock combination conditions.