1507 results about "Learning based" patented technology

A method for identifying a botnet in a network, including analyzing historical network data using a pre-determined heuristic to determine values of a feature in the historical network data, obtaining a ground truth data set having labels assigned to data units in the historical network data identifying known malicious nodes in the network, analyzing the historical network data and the ground truth data set using a machine learning algorithm to generate a model representing the labels as a function of the values of the feature, analyzing real-time network data using the pre-determined heuristic to determine a value of the feature for a data unit in the real-time network data, assigning a label to the data unit by applying the model to the value of the feature, and categorizing the data unit as associated with the botnet based on the label.

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.

A method for identifying a botnet in a network, including analyzing historical network data using a pre-determined heuristic to determine values of a connectivity graph based feature in the historical network data, obtaining a ground truth data set having labels assigned to data units in the historical network data identifying known malicious nodes in the network, analyzing the historical network data and the ground truth data set using a machine learning algorithm to generate a model representing the labels as a function of the values of the connectivity graph based feature, analyzing real-time network data using the pre-determined heuristic to determine a value of the connectivity graph based feature for a data unit in the real-time network data, assigning a label to the data unit by applying the model to the value of the connectivity graph based feature, and categorizing the data unit as associated with the botnet based on the label.

Methods and systems for image registration using an intelligent artificial agent are disclosed. In an intelligent artificial agent based registration method, a current state observation of an artificial agent is determined based on the medical images to be registered and current transformation parameters. Action-values are calculated for a plurality of actions available to the artificial agent based on the current state observation using a machine learning based model, such as a trained deep neural network (DNN). The actions correspond to predetermined adjustments of the transformation parameters. An action having a highest action-value is selected from the plurality of actions and the transformation parameters are adjusted by the predetermined adjustment corresponding to the selected action. The determining, calculating, and selecting steps are repeated for a plurality of iterations, and the medical images are registered using final transformation parameters resulting from the plurality of iterations.

A method and system for determining fractional flow reserve (FFR) for a coronary artery stenosis of a patient is disclosed. In one embodiment, medical image data of the patient including the stenosis is received, a set of features for the stenosis is extracted from the medical image data of the patient, and an FFR value for the stenosis is determined based on the extracted set of features using a trained machine-learning based mapping. In another embodiment, a medical image of the patient including the stenosis of interest is received, image patches corresponding to the stenosis of interest and a coronary tree of the patient are detected, an FFR value for the stenosis of interest is determined using a trained deep neural network regressor applied directly to the detected image patches.

In accordance with various embodiments of the disclosed subject matter, a method and a system for vision-centric deep-learning-based road situation analysis are provided. The method can include: receiving real-time traffic environment visual input from a camera; determining, using a ROLO engine, at least one initial region of interest from the real-time traffic environment visual input by using a CNN training method; verifying the at least one initial region of interest to determine if a detected object in the at least one initial region of interest is a candidate object to be tracked; using LSTMs to track the detected object based on the real-time traffic environment visual input, and predicting a future status of the detected object by using the CNN training method; and determining if a warning signal is to be presented to a driver of a vehicle based on the predicted future status of the detected object.

Information retrieval systems face challenging problems with delivering highly relevant and highly inclusive search results in response to a user's query. Contextual personalized information retrieval uses a set of integrated methodologies that can combine automatic concept extraction / matching from text, a powerful fuzzy search engine, and a collaborative user preference learning engine to provide accurate and personalized search results. The system can include constructing a search query to execute a search of a database parsing an input query from a user conducting the search of the database into sub-strings, and matching the sub-strings to concepts in a semantic concept network of a knowledge base. The system can further map the matched concepts to criteria and criteria values that specify a set of constraints on and scoring parameters for the matched concepts. Furthermore, the system can learn user preferences to construct one or more profiles for producing personalized search results.

A machine learning based electronic mail system. A classifier and action selection module analyzes the incoming message and classifies the messages with associated confidence levels, which may include analyzing the electronic message by tokenization of the text, morphological analysis of the text, and other well known processes. The classifier and action selection module then determines the appropriate action or actions to effect on the message.

A smart home interaction system is presented. It is built on a multi-modal, multithreaded conversational dialog engine. The system provides a natural language user interface for the control of household devices, appliances or household functionality. The smart home automation agent can receive input from users through sensing devices such as a smart phone, a tablet computer or a laptop computer. Users interact with the system from within the household or from remote locations. The smart home system can receive input from sensors or any other machines with which it is interfaced. The system employs interaction guide rules for processing reaction to both user and sensor input and driving the conversational interactions that result from such input. The system adaptively learns based on both user and sensor input and can learn the preferences and practices of its users.

An interactive machine learning based system that incrementally learns, on the basis of text data, how to annotate new text data. The system and method starts with partially annotated training data or alternatively unannotated training data and a set of examples of what is to be learned. Through iterative interactive training sessions with a user the system trains annotators, and these are in turn used to discover more annotations in the text data. Once all of the text data or a sufficient amount of the text data is annotated, at the user's discretion, the system learns a final annotator or annotators, which are exported and available to annotate new textual data. As the iterative training process occurs the user is selectively presented for review and appropriate action, system-determined representations of the annotation instances and provided a convenient and efficient interface so that context of use can be verified if necessary in order to evaluate the annotations and correct them, where required. At the user's discretion, annotations that receive a high confidence level can be automatically accepted and those with low confidence levels can be automatically rejected.

According to various implementations, a demand response (DR) strategy system is described that can effectively model the HVAC energy consumption of a house using a learning based approach that is based on actual energy usage data collected over a period of days. This modeled energy consumption may be used with day-ahead energy pricing and the weather forecast for the location of the house to develop a DR strategy that is more effective than prior DR strategies. In addition, a computational experiment system is described that generates DR strategies based on various energy consumption models and simulated energy usage data for the house and compares the cost effectiveness and energy usage of the generated DR strategies.

Methods and systems for performing one or more functions for a specimen using output simulated for the specimen are provided. One system includes one or more computer subsystems configured for acquiring output generated for a specimen by one or more detectors included in a tool configured to perform a process on the specimen. The system also includes one or more components executed by the one or more computer subsystems. The one or more components include a learning based model configured for performing one or more first functions using the acquired output as input to thereby generate simulated output for the specimen. The one or more computer subsystems are also configured for performing one or more second functions for the specimen using the simulated output.

Methods and systems for generating simulated output for a specimen are provided. One method includes acquiring information for a specimen with one or more computer systems. The information includes at least one of an actual optical image of the specimen, an actual electron beam image of the specimen, and design data for the specimen. The method also includes inputting the information for the specimen into a learning based model. The learning based model is included in one or more components executed by the one or more computer systems. The learning based model is configured for mapping a triangular relationship between optical images, electron beam images, and design data, and the learning based model applies the triangular relationship to the input to thereby generate simulated images for the specimen.

he invention discloses a medical information extraction system and method based on depth learning and distributed semantic features. The system is composed of a pretreatment module, a linguistic-model-based word vector training module, a massive medical knowledge base reinforced learning module, and a depth-artificial-neural-network-based medical term entity identification module. With a depth learning method, generation of the probability of a linguistic model is used as an optimization objective; and a primary word vector is trained by using medical text big data; on the basis of the massive medical knowledge base, a second depth artificial neural network is trained, and the massive knowledge base is combined to the feature leaning process of depth learning based on depth reinforced learning, so that distributed semantic features for the medical field are obtained; and then Chinese medical term entity identification is carried out by using the depth learning method based on the optimized statement-level maximum likelihood probability. Therefore, the word vector is generated by using lots of unmarked linguistic data, so that the tedious feature selection and optimization adjustment process during medical natural language process can be avoided.

The invention provides a face-based automatic demographics classification system that is robust to pose changes of the target faces and to accidental scene variables, by using a pose-independent facial image representation which comprises multiple pose-dependent facial appearance models. Given a sequence of people's faces in a scene, the two-dimensional variations are estimated and corrected using a novel machine learning based method. We estimate the three-dimensional pose of the people, using a machine learning based approach. The face tracking module keeps the identity of the person using geometric and appearance cues, where multiple appearance models are built based on the poses of the faces. Each separately built pose-dependent facial appearance model is fed to the demographics classifier, which is trained using only the faces having the corresponding pose. The classification scores from the set of pose-dependent classifiers are aggregated to determine the final face category, such as gender, age, and ethnicity.

Methods and systems for accelerated training of a machine learning based model for semiconductor applications are provided. One method for training a machine learning based model includes acquiring information for non-nominal instances of specimen(s) on which a process is performed. The machine learning based model is configured for performing simulation(s) for the specimens. The machine learning based model is trained with only information for nominal instances of additional specimen(s). The method also includes re-training the machine learning based model with the information for the non-nominal instances of the specimen(s) thereby performing transfer learning of the information for the non-nominal instances of the specimen(s) to the machine learning based model.

Face representation is a crucial step of face recognition systems. An optimal face representation should be discriminative, robust, compact, and very easy to implement. While numerous hand-crafted and learning-based representations have been proposed, considerable room for improvement is still present. A very easy-to-implement deep learning framework for face representation is presented. The framework bases on pyramid convolutional neural network (CNN). The pyramid CNN adopts a greedy-filter-and-down-sample operation, which enables the training procedure to be very fast and computation efficient. In addition, the structure of Pyramid CNN can naturally incorporate feature sharing across multi-scale face representations, increasing the discriminative ability of resulting representation.

A learning-based method for estimating costs or statistics of an operator in a continuous query includes a cost estimation model learning procedure and a model applying procedure. The model learning procedure builds a cost estimation model from training data, and the applying procedure uses the model to estimate the cost associated with a given query. The learning procedure uses a feature extractor and a cost estimator. The feature extractor collects relevant training data and obtains feature values. The extracted feature values are associated with costs and used to create the cost estimator. When applying the cost estimator to a continuous stream of data, the feature extractor extracts feature values from the data stream and uses the extracted feature values as inputs into the cost estimator to obtain the desired cost values.

The present invention relates to methods and systems for the exhibition of a motion picture with enhanced perceived resolution and visual quality. The enhancement of perceived resolution is achieved both spatially and temporally. Spatial resolution enhancement creates image details using both temporal-based methods and learning-based methods. Temporal resolution enhancement creates synthesized new image frames that enable a motion picture to be displayed at a higher frame rate. The digitally enhanced motion picture is to be exhibited using a projection system or a display device that supports a higher frame rate and / or a higher display resolution than what is required for the original motion picture.

A learning marker configured such that one of learning horizontal distance information and learning vertical distance information can be learned based on detection information from a horizontal travel distance detection device or a vertical travel distance detection device upon detection of at least one edge section of the learning marker in one direction of a horizontal travel direction and a vertical travel direction of a transfer means by a learning marker detector for detecting the learning marker, and the other of the learning horizontal distance information and learning vertical distance information can be learned based on detection information from the horizontal travel distance detection device or the vertical travel distance detection device upon detection of each of both edge sections of the learning marker in the one direction by the learning marker detector.

A computing device receives a training data set that includes a plurality of positive examples of sensitive data and a plurality of negative examples of sensitive data via a user interface. The computing device analyzes the training data set using machine learning to generate a machine learning-based detection (MLD) profile that can be used to classify new data as sensitive data or as non-sensitive data. The computing device displays a quality metric for the MLD profile in the user interface.

Systems and techniques are provided for tracking inventory items in an area of real space. A plurality of cameras, or other sensors, produce respective sequences of images in corresponding fields of view in the real space. The field of view of each camera overlaps with the field of view of at least one other camera. The system is coupled to the plurality of cameras and uses the sequences of images produced by at least two cameras in the plurality of cameras to identify inventory events. The inventory event includes an item identifier, a location and a timestamp. A plurality of cells having coordinates in the area of real space are stored as a data set in the memory. The processing system calculates scores at a scoring time, for inventory items having locations matching particular cells using respective counts of inventory events.

The invention discloses a machine-learning-based air conditioner control method and device as well as an air conditioner. The air conditioner control method comprises the steps that operation parameters and set time, set by a user, of the air conditioner are acquired; when the user sets the operation parameters of the air conditioner, environment parameters corresponding to the set time is obtained; the operation parameters, recorded repeatedly, of the air conditioner are used as the output, the corresponding environment parameters are used as the input, and training is carried out on a neuralnetwork control model so as to obtain a trained neural network control model; and control is carried out on the air conditioner according to the trained neural network control model. According to themachine-learning-based air conditioner control method, self learning of behaviors of controlling the air conditioner by the user can be achieved, and intelligentization of air conditioner control isimproved; when the air conditioner is started, the user does not need to input a control parameter, and the air conditioner can be started through one key, so that complex operation is avoided; and full automatic control of the air conditioner is even realized.

An automated blocking technique is used as a first step to find approximate matches in a database. The technique builds a blocking set to be as liberal as possible in retrieving records that match on individual fields or sets of fields while avoiding selection criteria that are predicted to return more than the maximum number of records defining a particular special requirement. The ability to do blocking without extensive manual setup at low cost is highly advantageous especially when using a machine learning based second-stage matching algorithm.

A search engine for searching based on related scientific or technological concepts, comprises: a learning module for learning about relationships between technical phrases based on their rates of occurrence in related documents, therefrom to form concepts from groupings of related phrases, and a search module for searching for related documents to a query document based on occurrence in said related documents of concepts present in said query document, the learning module carrying out said learning based on a training set of documents and inter-document relations.

Systems and techniques are provided for monitoring status of subjects in an area of real space. The system includes a display having access to memory storing a first data set identifying locations of subjects in the area of real space over time, a second data set identifying item put and take events in the area of real space and a third data set identifying accounts matched to the subjects in the first data set. The system includes a graphical user interface logic that renders a map on the display of at least part of the area of real space including avatars having locations on the map corresponding to locations in the area of real space according to the first data set, and identifying respective statuses assigned to the subjects based on one or more of the first data set, the second data set and the third data set.

A computing system uses neural networks to translate natural language queries to database queries. The computing system uses a plurality of machine learning based models, each machine learning model for generating a portion of the database query. The machine learning models use an input representation generated based on terms of the input natural language query, a set of columns of the database schema, and the vocabulary of a database query language, for example, structured query language SQL. The plurality of machine learning based models may include an aggregation classifier model for determining an aggregation operator in the database query, a result column predictor model for determining the result columns of the database query, and a condition clause predictor model for determining the condition clause of the database query. The condition clause predictor is based on reinforcement learning.