783 results about "Class information" patented technology

A method and apparatus are provided for updating the vocabulary of a speech translation system for translating a first language into a second language including written and spoken words. The method includes adding a new word in the first language to a first recognition lexicon of the first language and associating a description with the new word, wherein the description contains pronunciation and word class information. The new word and description are then updated in a first machine translation module associated with the first language. The first machine translation module contains a first tagging module, a first translation model and a first language module, and is configured to translate the new word to a corresponding translated word in the second language. Optionally, the invention may be used for bidirectional or multi-directional translation

Methods, apparatuses and systems directed to the coordinated classification of network traffic. In one implementation, the present invention enables a coordinated network environment for traffic classification where an upstream network device classifies a data flow and adds traffic class information to at least one packet in the data flow. Downstream network devices in the communications path to the destination host can use the traffic class information in the modified packet, bypassing at least some of the local traffic classification operations and thereby reducing CPU utilization. In one implementation, the last downstream network device strips the traffic classification information from the modified packet before it is forwarded to the destination host. Embodiments of the invention reduce or eliminate redundant network traffic classification operations performed by a plurality of network devices in a communications path.

Methods, systems, and articles of manufacture consistent with certain principles related to the present invention enable a computing system to layer the packaging and deployment of a web service endpoint on a standard servlet component model. To define a web service endpoint, the computing system may develop a service endpoint class that implements a configured service endpoint interface. The interface and endpoint class may be packaged by the computing system into an archive file. The computing system may use the information stored in the archive file to define the service endpoint modify the archive file with class information associated with the defined service endpoint. The modified archive file may be deployed on a container operating within the computing system. Further, the computing system may produce a WSDL based description of the defined service endpoint and export the document such that a remote client may use the information contained in the document to locate and remotely invoke methods on the defined service endpoint.

Natural-quality synthetic noise will replace background acoustic noise during speech gaps and will achieve a better representation of the excitation signal in a noise-synthesis model by classifying the type of acoustic environment noise into one or more of a plurality of noise classes. The noise class information is used to synthesize background noise that sounds similar to the actual background noise during speech transmission. In some embodiments, the noise class information is derived by the transmitter and transmitted to the receiver which selects corresponding excitation vectors and filters them using a synthesis filter to construct the synthetic noise. In other embodiments, the receiver itself classifies the background noise present in hangover frames and uses the class information as before to generate the synthetic noise. The improvement in the quality of synthesized noise during speech gaps helps to preserve noise continuity between talk spurts and speech pauses, and enhances the perceived quality of a conversation.

An ontology-based ad hoc service discovery system includes a local service cache, a cache manager, a service description unit, a query processor, a service semantic inference unit and a node daemon. The local service cache restores a service ontology by collecting class information of all services advertised on an ad hoc network and stores the service ontology. The cache manager manages the local service cache and performs various preset operations on the cache. The service description unit stores a description of a corresponding service for use in initializing the local service cache. The query processor starts performing a semantic based service query protocol by receiving a service query from a user or an application program. The service semantic inference unit inspects whether the service query transmitted from a client is coincident with the content of the service. The node daemon performs a service cache synchronization protocol with neighboring nodes.

The invention discloses a subjective text emotion analysis method based on deep learning. The method includes the steps that 1, a C&W-SP model is established based on a C&W model, an emotion label and a word class label of a sentence are labeled in the sentence, a training set of a C&W_SPC&W-SP model is established, a C&W_SP model is trained through the training set, a word vector of each word in the training set is obtained, and a word vector file is formed; 2, a sentence vector set is established through an LSTM model according to the obtained word vector file; 3, a neutral network model is trained through the sentence vector set, and an emotion classification model is obtained; 4, the tested comment sentence is preprocessed, the tested sentence vectors are input in the emotion classification model, and the emotion tendency of the section of comment is obtained through calculation. According to the method, emotion tendency information and word class information are added into words, and the accuracy of emotion analysis is improved.

An object oriented mechanism is disclosed that allows class replacement that is based on a specified processing context by replacing existing configuration data with new configuration data within an existing object oriented program. The configuration data is preferably stored in a data structure external to the class definitions to allow modifying the classes without re-compiling the class code. The configuration data includes multiple entries, where each entry is a key-value pair. The key includes information relating to the processing context, and the value includes information relating to the class that corresponds to the key. When an instance of a class needs to be created, the context information is retrieved, a key is generated from a combination of the class information and the context information, and the context-sensitive key is used to retrieve the corresponding class configuration data from the data structure that specifies a particular class. An object that is an instance of the specified class is then created. By generating a key that is context-specific, configuration data for a class can be scoped to a particular processing context, thereby providing context-based class replacement.

According to a first aspect there is provided a system to automatically generate software for an object to relational mapping system. The system automatically generates a number of classes. The system automatically generates the each class by reading class information, reading meta-data based on the class information, and automatically generating the class based on the meta-data. The classes used in the object to relational mapping system to enable an application program to access data that is persistently stored in a database and accessed by the application program from an instance of a data object class that is included in the classes.

A first mobile device (for example, a cellular telephone) learns of the presence of second mobile devices (for example, other cellular telephones) that are located nearby the first mobile device. A relatively short range wireless communication mechanism (for example, Bluetooth) of the mobile devices may be used to discover other nearby devices automatically by establishing direct mobile-to-mobile communications without use of the cellular telephone functionality of the mobile devices. Regardless of how the presence of other nearby mobiles is detected, information on the presence of these nearby mobiles can be matched with other information on the users of the nearby mobiles such as user class information. The collected information is then used as an input to a context-aware functionality. The context-aware functionality applies a rule that causes the first mobile to be configured in a way that is better suited the situation and / or to who the other nearby cellular telephone users are and what they are doing.

An approach for managing system capacity of a satellite communications system is disclosed. A satellite communications system provides communication services to a region. A terminal (101, 103) is located within the region and is configured to transmit and receive signals over a satellite (105) having a payload that processes the signals. The terminal (101, 103) has a predetermined profile that includes service class information and rate information. A hub (107) is configured to receive system capacity resource configuration data that reflect capacity requirements of a service provider and to determine partitioning of system capacity over the region based upon the system capacity resource configuration data. The hub (107) transmits configuration information to the payload of the satellite (105) according to the determined partitions. The terminal (101, 103) is configured to transmit a bandwidth request message to the payload. The payload selectively allocates bandwidth in response to the request message based upon the configuration information.

In a method of detecting motion of a particular part from a sequence of successive input images, motion vectors of a plurality of parts of the input images are detected by tracking feature points in the successive input images, the motion of the particular part is detected by processing the motion vectors. The motion detection is performed by producing a histogram of the motion vectors, detecting a peak corresponding to the particular part from the histogram; classifying feature points into a class of feature points belonging to the peak detected in the histogram processing step and a class of the other feature points, and calculating motion of the particular part by analysing the motion vectors belonging to the peak detected in the histogram processing step. The histogram processing step includes detecting the peak corresponding to the particular part on the basis of the class information defined for feature points.

According to a second aspect there is provided a method and system to automatically regenerate software for an object to relational mapping system. The system automatically regenerates a first set of classes. For each class, the system reads class information; reads meta-data based on the class information; and automatically regenerates the class based on the meta-data without overwriting associated custom code. The first set of classes includes base code that is associated with the custom code. The first set of classes and the base code are utilized by the object to relational mapping system to enable an application program to access data that is persistently stored in a database.

The present invention provides a client user groups clustering method comprising the steps of collecting multiple user word banks from input method client users, recoding the corresponding relationship between a user and the corresponding user word bank, wherein, the word bank comprises words and word frequency; calculating to obtain characteristic parameters of a user according to the word bank of the user; calculating similarity degree between the user characteristic parameters to complete the clustering of user groups. Furthermore, the present also comprises a step of providing personalized information service to a user according to class information of the user. According to the present invention, class identification of users is completed by collecting input information (the input information includes input content and input habit) of multiple users and through a clustering strategy with a higher accuracy, and the clustering accuracy of input method client user groups are improved, therefore, personalized information services for users can be provided and the accuracy can be assured.

The application discloses an application customizing method and an application customizing device. One specific embodiment of the method comprises the following steps: firstly acquiring biologic feature information parameters which correspond to an unlocking instruction; then, according to a preset numerical value interval to which the biologic feature information parameters belong, determining group class information; finally, according to a preset operation privilege level which corresponds to the group class information, performing application initial configuration. The application initial configuration suitable for different user classes can be performed adaptively, and the goal that different application customizing can be performed by aiming at different groups and the same terminal is achieved, and therefore waste of resources can be avoided.

A recruitment enhancement system is disclosed that can be used by job recruiters to assess job applicant's suitability for particular jobs. The recruitment enhancement system can maintain and regularly update a proprietary database including resume data and data gathered from social media sources for various individuals. The system can be configured to filter data before it is presented to a user. The filtering system can be configured such that only data “safe” for use in a hiring decision is presented about individuals. In one embodiment, the filtering can involve identifying protected class information for each individual and preventing its output via the system.

The invention proposes a method and a device for generating a test case. The method comprises the following steps: obtaining an input instrument and a configuration film, wherein the input instruction comprises a test type parameter; the configuration film comprises a module, class information, function information of the tested function and a test parameter; determining the test type according to the test type parameter, and structuring test data according to the test type and the test parameter; rendering the preset template according to the template, the class information, the function information of the tested function and the test data, so as to generate the test case. By adopting the method disclosed by the embodiment of the invention, on one hand, automatic generation of the function test case is achieved without artificially compiling the test case of the tested function, so as to improve the test accuracy, and on the other hand, the test type is determined according to the test type parameter, and support of a plurality of different test modes is achieved, so that the supported test modes are diversified.

The application relates to a training method and device of a classification model, a mobile terminal, and a computer readable storage medium. The method comprises the following steps: training the classification model based on a preset data set until the precision of the classification model meets the standard value, wherein the data in the preset data set carries annotation information; identifying each data in the preset data set based on the trained classification model so as to acquire class information of each data; when the class information of the data and the annotation information areinconsistent, cleaning the data so as to acquire the cleaned target data set; re-training the classification model based on the cleaned target data set, thereby guaranteeing the quality of each datain the target data set based on a semi-automatic cleaning way. The data quality can be guaranteed without performing multi-stage artificial auditing mechanism, the manpower cost is greatly saved, thedata cleaning efficiency is improved, and the precision and the performance of the classification model can be improved by training the classification model based on the target data set.

A method for adapting a .Net framework compliant reflection mechanism to a Java environment, wherein the reflection mechanism allows a user to discover class information and invoke data members solely at runtime. The method includes gathering reflection related metadata from a .Net assembly class of a respective .Net class that comprises the metadata. The method also includes the steps of generating an attributes class comprising at least the reflection related metadata and a plurality of conversion methods and integrating the attributes class in a Java class file of the respective .Net class, such that the reflection mechanism is capable of adapting the reflection mechanism as defined in the .Net framework to the Java environment. Also disclosed is a plurality of conversion methods. The attributes class is included in a Java class file rendered by a compiler capable of compiling a .Net class to a Java compliant programming code.

This invention discloses a pushing system for sorted messages including a server and a customer end, in which, the server includes a storage module storing sorted information, said customer end includes a message-sorting tool in a browsing server, an exclusive network identification, a statistic module calculating all kinds of sorted messages browsed by the customer end and a pushing module pushing all kinds of sorted messages in terms of priority sequence to the customer end having network identification. This invention also discloses a corresponding push method.

A storage device includes a semiconductor memory storing data. A controller instructs to write data to the semiconductor memory in accordance with a request the controller receives. A register holds performance class information showing one performance class required to allow the storage device to demonstrate best performance which the storage device supports, of performance classes specified in accordance with performance.

A system that uses a Uniform Resource Identifier (URI) to uniquely identify a class of resources. The schema uses a URI with a placeholder substituted for the resource name, effectively creating a URI template. Processing of the URI template facilitates retrieval of resource class information. If specificity is required, the URI template can be converted to a specific URI by replacing the placeholder with the name of a given resource or instance to get information about that particular instance. URI templates allow probes to be identified and the probe characteristics to be understood without actually retrieving the probe for a particular instance.

A web page prefetching apparatus and method for efficiently prefetching a web page using link class information indicating preference of client devices for links contained in a web page. A client device includes a user input unit receiving a user input signal for a web page request; a controller receiving a first web page provided by a first web server based on a user input signal requesting the first web page, requesting a second web page provided by a second web server from the second web server, which is determined based on link class information indicating the preference of client devices for links contained in the first web page, without a user input signal, and receiving the second web page from the second web server; and a storage unit storing the second web page. Accordingly, since the reliability and efficiency of prefetching can be increased, prefetched web pages can be quickly provided to a user.

The invention belongs to the technical field of neural network, and provides a method and apparatus for increasing generalization capability of a convolutional neural network. The method includes the following steps: reading a group of images from a training set, mapping the group of images to a plurality of image character vectors, dividing the plurality of image characteristic vectors into a plurality of classes based on the types of the images; based on the image characteristic vectors of each class, calculating the intra-class loss function of all the characteristic vectors; based on the image characteristic vectors of each class, calculating the inter-class loss function of all the characteristic vectors; based on the intra-class loss function of all the characteristic vectors, using the back propagation algorithm to update the weight of each node of the convolutional neural network; repeating the above mentioned steps until the convolutional neural network converges on the training set or reaches preset repeating times. According to the invention, the method and the apparatus can save all data in long-tailed distribution, makes full usage of abundant inter-class information of tail data, and increases the generalization capability of the convolutional neural network.

The invention discloses a semi-supervised intrusion detection method based on a depth generation model. The method comprises the steps of: 1, preprocessing data: converting symbol attributes in a dataset into numerical attributes, and then normalizing all the numerical attributes; 2, converting high-dimensional feature representations of labeled and unlabeled data into low-dimensional representations of a new feature space by using the variational self-encoding technology in the generation model, adding a constraint to low-dimensional feature vectors to obey Gaussian positive distribution soas to obtain a hidden variable z, and training a classifier by using the hidden variable z in combination with a labeled sample; 3, reconstructing labeled sample data: jointly generating a new labeledsample by using the hidden variable z in combination with label class information; 4, reconstructing an unlabeled sample: predicting the probability of each class of an unlabeled sample by using thehidden variable z, and then generating a new unlabeled sample in combination with the hidden variable z; and 5, calculating a reconstruction error of the model with the newly generated labeled and unlabeled samples, and training and optimizing model parameters in combination with a classification error till convergence.

The invention relates to the technical field of computer vision, in particular to an instance-level image search method based on multiple layers of feature representations. According to the method, feature representations, on different layers, of images are learnt through a deep convolution neural network model for instance-level image search so that different images of the same article can be effectively found. A coding learning process is introduced on the basis of a traditional network model, and features from multiple convolution layers are automatically encoded, so that the extracted features are more robust, and influences of background and noise data on the features are reduced. Meanwhile, the features integrate local information and class information. The invention further discloses a loss function based on multiple tasks. By optimizing the function, the learnt features have good generalization performance, and the learnt features can be well used for distinguishing inter-class images from intra-class images of different things.

A method and apparatus are provided for updating the vocabulary of a speech translation system for translating a first language into a second language including written and spoken words. The method includes adding a new word in the first language to a first recognition lexicon of the first language and associating a description with the new word, wherein the description contains pronunciation and word class information. The new word and description are then updated in a first machine translation module associated with the first language. The first machine translation module contains a first tagging module, a first translation model and a first language module, and is configured to translate the new word to a corresponding translated word in the second language. Optionally, the invention may be used for bidirectional or multi-directional translation.

The invention relates to a network-based system for analyzing opinion information in a discrete text, belonging to the field of network information safety. The system comprises the following modules: a discrete text information acquisition module which acquires network information in a preset analysis cycle, a discrete text information tracking and restoring module which restores ellipsis and remote anaphora in the original content to obtain a text which contains a relatively complete text structure and semantic information, a semantic information mining and characteristic extracting module which realizes semantic information mining and characteristic extracting on text information by utilizing a latent semantic indexing technology, an opinion information clustering module which realizes information clustering by combining a niche genetic algorithm with a K-Means method, a hot opinion event discovery module which mines the hot opinion in the obtained topic and event, and a background information processing and data supporting center which analyzes data and provides a repertoire specially for a network, new words in the network, the existing class information and the existing hot topics. By applying the invention, the problem that information analysis is influenced as the text structure of the existing network opinion information is incomplete, ellipsis and remote anaphora are more and the new works in the network are more is solved, and the accuracy for discovery of the opinion and hot event is improved by adopting a high-efficiency clustering method.

The invention provides a ground object classification method based on electric power corridor airborne LiDAR point cloud dada, and relates to the airborne LiDAR point cloud data processing field. In order to solve the problem of insufficient utilization of information included in the electric power corridor airborne LiDAR point cloud dada in the prior art, the invention provides a ground object classification method based on electric power corridor airborne LiDAR point cloud data features. The method comprises the following steps: obtaining airborne LiDAR point cloud dada; removing gross error points of the point cloud dada; carrying out point cloud dada feature extraction and processing, wherein the point cloud dada features comprise single point feature and neighbourhood feature, and carrying out normalization processing on the extracted features; and selecting samples having labels from a LiDAR point cloud dada set, separating target information of power lines, vegetation, buildings and earth surface and the like in the LiDAR point cloud dada, and classifying the data set by utilizing the samples having labels to obtain class information of power lines, vegetation, buildings and earth surface and the like.