708 results about "Training phase" patented technology

Embodiments of the invention address the problem of detecting bots in network traffic based on a classification model learned during a training phase using machine learning algorithms based on features extracted from network data associated with either known malicious or known non-malicious client and applying the learned classification model to features extracted in real-time from current network data. The features represent communication activities between the known malicious or known non-malicious client and a number of servers in the network.

An apparatus and method for detection of abnormal motion in video stream, comprising a training phase for defining normal motion and a detection phase for detecting abnormal motions in the video stream. Motion is detected according to motion vectors and motion features extracted from video frames.

The invention discloses a pedestrian re-identification method based on multi-attribute and multi-strategy fusion learning. The method of the invention includes the steps of in an offline training phase, firstly selecting pedestrian attributes which are easy to be judged and have a sufficient distinguishing degree, training a pedestrian attribute identifier on an attribute data set, then labeling attribute tags for a pedestrian re-identification data set by using the attribute identifier, and next, by combining the attributes and pedestrian identity tags, training a pedestrian re-identification model by using a strategy fused with pedestrian classification and novel constraint comparison verification; and in an online query phase, extracting features of a query image and images in a database by using the pedestrian re-identification model, and calculating the Euclidean distance between the feature of the query image and the feature of each image in the database to obtain the image with the shortest distance, which is considered as the result of pedestrian re-identification. In terms of performance, the features in the invention are distinguishable and high accuracy is obtained; and in terms of efficiency, the method of the invention can quickly search for the pedestrian indicated by the query image from the pedestrian image database.

A user authentication method for access to a mobile user terminal including one or more movement sensor supplying information on movements of the mobile terminal, includes authenticating a user on the basis of a movement performed handling the mobile terminal. A test pattern of values is sensed by one or more movement sensor related to a movement performed in a predetermined condition corresponding to an action for operating the mobile terminal. In a biometric recognition phase the acquired test pattern is compared to a stored pattern of values obtained from the one or more movement sensor by a training phase executed by an accepted user performing the action for operating the mobile terminal. The comparison includes measuring a similarity of the acquired test pattern to stored pattern. An authentication result is obtained by comparing the measured similarity to a threshold.

A system and a method for detecting and diagnosing faults in heating, ventilating and air conditioning (HVAC) equipment is described. The system comprises a sensor; a classifier modelling a normal behaviour of the HVAC equipment in situ in the installed operation environment, the classifier having a plurality of classifier parameters for computing a classifier score using an input data based on a measured value from the sensor, the plurality of classifier parameters being created during a training phase of the system using the input data during the training phase; and a decision module for comparing the classifier score to a decision threshold, the decision threshold being set during the training phase.

A computer readable storage medium includes executable instructions to insert monitors at selected locations within a computer program. Training output from the monitors is recorded during a training phase of the computer program. Program invariants are derived from the training output. During a deployment phase of the computer program, deployment output from the monitors is compared to the program invariants to identify security anomalies.

A time to event data analysis method and system. The present invention relates to the analysis of data to identify relationships between the input data and one or more conditions. One method of analysing such data is by the use of neural networks which are non-linear statistical data modelling tools, the structure of which may be changed based on information that is passed through the network during a training phase. A known problem that affects neural networks is the issue of overtraining which arises in overcomplex or overspecified systems when the capacity of the network significantly exceeds the needed parameters. The present invention provides a method of analysing data, such as bioinformatics or pathology data, using a neural network with a constrained architecture and providing a continuous output that can be used in various contexts and systems including prediction of time to an event, such as a specified clinical event.

Deep belief networks are usually associated with a large number of parameters and high computational complexity. The large number of parameters results in a long and computationally consuming training phase. According to at least one example embodiment, low-rank matrix factorization is used to approximate at least a first set of parameters, associated with an output layer, with a second and a third set of parameters. The total number of parameters in the second and third sets of parameters is smaller than the number of sets of parameters in the first set. An architecture of a resulting artificial neural network, when employing low-rank matrix factorization, may be characterized with a low-rank layer, not employing activation function(s), and defined by a relatively small number of nodes and the second set of parameters. By using low rank matrix factorization, training is faster, leading to rapid deployment of the respective system.

The invention discloses a face image restoration method based on a generation antagonism network. The method comprises the following steps: a face data set is preprocessed, and a face image with a specific size is obtained by face recognition of the collected image; In the training phase, the collected face images are used as dataset to train the generating network and discriminant network, aimingat obtaining more realistic images through the generating network. In order to solve the problems of instability of training and mode collapse in the network, the least square loss is used as the loss function of discriminant network. In the repairing phase, a special mask is automatically added to the original image to simulate the real missing area, and the masked face image is input into the optimized depth convolution to generate an antagonistic network. The relevant random parameters are obtained through context loss and two antagonistic losses, and the repairing information is obtainedthrough the generated network. The invention can not only solve the face image repairing with serious defective information, but also generate a face repairing image which is more consistent with visual cognition.

A method for detecting a SQL injection attack comprises a training phase and a detection phase. In the training phase, a plurality of SQL queries is transformed into a respective plurality of SQL token domain queries which are processed using a n-gram analysis to provide a threshold and an averaging vector. In the detection phase, each newly arrived SQL query is transformed into a new SQL token domain query, and the n-gram analysis is applied together with the averaging vector and the threshold to each new SQL token domain query to determine if the new SQL query is normal or abnormal. The detection may be online or offline.

A prediction-based system and method for trajectory planning of autonomous vehicles are disclosed. A particular embodiment is configured to: receive training data and ground truth data from a training data collection system, the training data including perception data and context data corresponding to human driving behaviors; perform a training phase for training a trajectory prediction module using the training data; receive perception data associated with a host vehicle; and perform an operational phase for extracting host vehicle feature data and proximate vehicle context data from the perception data, generating a proposed trajectory for the host vehicle, using the trained trajectory prediction module to generate predicted trajectories for each of one or more proximate vehicles near the host vehicle based on the proposed host vehicle trajectory, determining if the proposed trajectory for the host vehicle will conflict with any of the predicted trajectories of the proximate vehicles, and modifying the proposed trajectory for the host vehicle until conflicts are eliminated.

The present invention is directed to systems and methods for isolating sentence boundaries between sentences in text. Sentences of the normalized document feeds or source text are separated by determining boundaries between individual sentences, by a Bayesian algorithm, that has been seeded with rule frequencies, developed from a previous training phase, that employed a text of sentences with marked boundaries between the sentences.

Techniques are disclosed for identifying anomaly object types during classification of foreground objects extracted from image data. A self-organizing map and adaptive resonance theory (SOM-ART) network is used to discover object type clusters and classify objects depicted in the image data based on pixel-level micro-features that are extracted from the image data. Importantly, the discovery of the object type clusters is unsupervised, i.e., performed independent of any training data that defines particular objects, allowing a behavior-recognition system to forgo a training phase and for object classification to proceed without being constrained by specific object definitions. The SOM-ART network is adaptive and able to learn while discovering the object type clusters and classifying objects and identifying anomaly object types.

A method for identifying documents for enriching a statistical translation tool includes retrieving a source document which is responsive to a source language query that may be specific to a selected domain. A set of text segments is extracted from the retrieved source document and translated into corresponding target language segments with a statistical translation tool to be enriched. Target language queries based on the target language segments are formulated. Sets of target documents responsive to the target language queries are retrieved. The sets of retrieved target documents are filtered, including identifying any candidate documents which meet a selection criterion that is based on co-occurrence of a document in a plurality of the sets. The candidate documents, where found, are compared with the retrieved source document for determining whether any of the candidate documents match the source document. Matching documents can then be stored and used at their turn in a training phase for enriching the translation tool.

The invention relates to a deep convolutional neural network-based traditional Chinese medicine tongue image automatic segmentation method and belongs to the computer vision field and traditional Chinese medicine tongue diagnosis field. According to the method of the invention, a convolutional neural network structure is designed; collected sample data are adopted to train the network, so that a network model can be obtained; and the model is adopted to automatically segment a traditional Chinese medicine tongue image. The method includes an offline training phase and an online segmentation phase. The method can be applied to both closed type and open tongue image acquisition environments and can effectively improve the accuracy and robustness of the automatic segmentation of the traditional Chinese medicine tongue image. The method of the present invention specifically relates to deep learning, semantic segmentation, image processing and other technologies.

Techniques for mitigating effects of differing latencies associated with real time data streams in multimedia communication networks. For example, a technique for mitigating a latency differential between a first media path and a second media path, over which a first device and a second device are able to communicate, includes the following steps. A training phase is performed to determine a latency differential between the first media path and the second media path. Prior to the first device switching a media stream, being communicated to the second device, from the first media path to the second media path, the first device synchronizes the media stream based on the determined latency differential such that a latency associated with the switched media stream is made to be substantially consistent with a latency of the second media path.

The invention relates to a depth study method for detecting salient regions in a natural image. During a training phase, a certain number of pictures are selected from a natural image database, basic features of the images are extracted to form a training sample, subsequently, the extracted features are studied by using a depth study model so as to obtain enhanced advanced features which are more abstractive and more distinguishable, and finally, a classifier is trained by using studied features. During a testing phase, as to any test image, firstly, the base features are extracted, secondly, the enhanced advanced features are extracted by using the trained depth model, finally, salience is predicted by using the classifier, and a predicted value of each pixel point serves as a salient value of the point. In such a way, a salient image of the whole image can be obtained, and the higher the salient value is, the more salient the image is.

The invention discloses a behavior identification method based on a 3D convolution neural network, and relates to the fields of machine learning, feature matching, mode identification and video image processing. The behavior identification method is divided into two phases including the off-line training phase and the on-line identification phase. In the off-line training phase, sample videos of various behaviors are input, different outputs are obtained through calculation, each output corresponds to one type of behaviors, parameters in the calculation process are modified according to the error between an output vector and a label vector so that all output data errors can be reduced, and labels are added to the outputs according to behavior names of the sample videos corresponding to the outputs after the errors meet requirements. In the on-line identification phase, videos needing behavior identification are input, calculation is conducted on the videos through the same method as the training phase to obtain outputs, the outputs and a sample vector for adding the labels are matched, and the name of the sample label most matched with the sample vector is viewed as a behavior name of the corresponding input video. The behavior identification method has the advantages of being low in complexity, small in calculation amount, high in real-time performance and high in accuracy.

The invention discloses a pedestrian-sensing obstacle avoidance method for a service robot based on deep reinforcement learning, and relates to the field of deep learning and service robot obstacle avoidance. In a training phase of the method, firstly, training data is generated by using an ORCA algorithm; then, an experimental scene is randomly generated, and an initialized reinforcement learningmodel is interacted with the environment to generate new training data to be merged into the original training data; finally, an SGD algorithm is used for training a network on the new training datato obtain a final network model. In an execution stage of the method, the state of the surrounding pedestrians is obtained by a laser radar, the predicted state is calculated according to the trainedmodel and the bonus function, and the action of obtaining the maximum reward is selected as the output and executed. The method has strong instantaneity and adaptability, in the pedestrian environment, the robot can comply with rules for pedestrians to walk on the right lines, an efficient, safe and natural path is planned, and the intelligence and sociality of the service robot are improved.

A search engine and system for data, such as Internet web pages, including a query analyser for processing a query to assign respective weights to terms of the query and to generate a query vector including the weights, and an index network responsive to the query vector to output at least one index to data in response to the query. The index network is a self-generating neural network built using training examples derived from a feature extractor. The feature extractor is used during both the search and training phase. A clusterer is used to group search results.