347 results about "Learning behavior" patented technology

Method and apparatus for correction of spelling errors in text composition is provided. Correctly spelled words may be determined for an incorrectly spelled word in accordance with a common error approach. Corrections to the incorrectly word are applied using common typographical and spelling errors. Resulting words which are correctly spelled may be offered as replacements or automatically used to replace the incorrectly word. Corrections may be applied in response to a frequency of the error in text composition to generate correct words that are more likely to be the word intended to be composed. The specific order of application may be configurable to meet a user's preference or a learned behavior for a user.

Embodiments of the present invention provide a method and a system for analyzing and learning behavior based on an acquired stream of video frames. Objects depicted in the stream are determined based on an analysis of the video frames. Each object may have a corresponding search model used to track an object's motion frame-to-frame. Classes of the objects are determined and semantic representations of the objects are generated. The semantic representations are used to determine objects' behaviors and to learn about behaviors occurring in an environment depicted by the acquired video streams. This way, the system learns rapidly and in real-time normal and abnormal behaviors for any environment by analyzing movements or activities or absence of such in the environment and identifies and predicts abnormal and suspicious behavior based on what has been learned.

A machine-learning engine is disclosed that is configured to recognize and learn behaviors, as well as to identify and distinguish between normal and abnormal behavior within a scene, by analyzing movements and / or activities (or absence of such) over time. The machine-learning engine may be configured to evaluate a sequence of primitive events and associated kinematic data generated for an object depicted in a sequence of video frames and a related vector representation. The vector representation is generated from a primitive event symbol stream and a phase space symbol stream, and the streams describe actions of the objects depicted in the sequence of video frames.

A system and method of operating a monitoring and response system for an actor in a daily living environment that relies upon learned models of behavior for adapting system operation. The learned model of behavior preferably includes sequential patterns organized pursuant to assigned partition values that in turn are generated based upon an evaluation of accumulated data. Based upon reference to the learned model of behavior, the system can generate more appropriate response plans based upon expected or unexpected activities, more readily recognize intended activities, recognize abandoned tasks, formulate probabilities of method choice, build probabilities of action success, anticipate and respond to actor movement within the environment, optimize response plan effectiveness, and share learned models across two or more separate system installations.

A system and method of non-interactive communication with wireless networks for a mobile wireless device such as a telephone, personal digital assistant (PDA), laptop, or other device configured for performing data transfers with an unreliable network, and the playing back of multimedia received over said network. The invention allows a user to send and receive email, web and other data updates in physical areas and locations of limited signal coverage without the user periodically activating the device for updating data. Furthermore, the invention allows non-interactive receipt of multimedia content for viewing when a network communication outage arises. In one embodiment a second (or subsequent) multimedia stream is received and stored in a data repository for playback when an outage arises. The programming of the device is configured to retrieve the content within the second multimedia stream in response to device programming, user set parameters, learned behaviors and / or non-user based selections.

Embodiments of the present invention provide a method and a system for mapping a scene depicted in an acquired stream of video frames that may be used by a machine-learning behavior-recognition system. A background image of the scene is segmented into plurality of regions representing various objects of the background image. Statistically similar regions may be merged and associated. The regions are analyzed to determine their z-depth order in relation to a video capturing device providing the stream of the video frames and other regions, using occlusions between the regions and data about foreground objects in the scene. An annotated map describing the identified regions and their properties is created and updated.

The invention provides an internet based method for recommending media courseware, which includes: obtaining courseware applicable attribute information by using a system terminal to analyze courseware attributes or courseware learning behavior data of users; determining preference information of the users by using the system terminal to analyze personal basic learning attribute information of the users or the courseware learning behavior data of the users; searching courseware lists suitable for the uses to learn according to the courseware applicable attribute information and the preference information of the users; and using a client to arrange and display the courseware lists searched from the system terminal according to the match degrees of the courseware lists suitable for the users to learn. The invention further provides an internet based system for recommending media courseware. By the aid of the internet based method and system for recommending media courseware, requirements of users on individual study can be met, precision and accuracy in recommending of suitable courseware to the users can be effectively improved, and the users can conveniently find the courseware suitable for learning.

The invention relates to a learning process that is personalized to the learner. Student performance and learning behavior is tracked and analyzed, feedback and reinforcement is given, and educational or training stakeholders are engaged. Continuous improvement of the student, instructor, curriculum, and learning process is envisioned to accelerate learning.

Vehicle behavior learning apparatuses, methods, and programs acquire image information of an area around a vehicle and perform image recognition of a particular feature included in the image information. The apparatuses, methods, and programs detect a behavior of the vehicle and acquire relation information indicating the relationship between the detected behavior of the and the recognized particular feature before the recognition of the behavior. The apparatuses, methods, and programs store detected behavior information including behavior property information indicating a property of the detected behavior of the vehicle and the acquired relation information associated with the behavior in a memory and produce learned behavior information indicating a result of learning of the behavior related to the particular feature on the basis of the stored detected behavior information.

A system for providing adaptive price optimization, comprising an adaptive neural network, the neural network further comprising a plurality of static and adaptive data, and a method for providing adaptive data learning behavior comprising the steps of making predictions based on data sources, comparing observed results to predicted results, and updating adaptive data sources.

Systems, networks, devices and methods for developing, implementing, and sharing lighting schemata between controllable lighting networks are disclosed. A network (101, 601, 701, 801, 808) in accordance with the disclosure stores lighting schemata developed for the network in a remote data store (802). Other networks (301) access the remote data store to select existing schemata for implementation. Systems, networks, devices, and methods for sharing user preferences between controllable lighting networks are also disclosed. Networks according to the disclosure may access a shared remote data store (112) to determine preferences of a user upon the detection, by sensors in the network, of the presence of the user. As such, individual lighting networks may make use of known user preferences or learned behaviors and environmental conditions to more efficiently adapt themselves to such behavior, preferences, or conditions.

The invention discloses a hierarchical network attack identification and unknown attack detection method based on deep learning, which comprises the following steps of: learning behavior modes of normal flow and abnormal flow by using an auto-encoder so as to judge whether the flow to be detected is the normal flow or the abnormal flow by using the learned auto-encoder; using a deep neural networkas a discrimination model to discriminate whether the attack type of the to-be-detected flow is a certain known attack type or an unknown type; and fusing the results of the auto-encoder and the discrimination model to complete network attack classification and unknown attack detection. According to the method, normal flow can be identified, known abnormal flow can be classified, novel unknown network attacks can be detected, and the detection accuracy is improved by combining the advantages of the generation model and the discrimination model.

Embodiments of the present invention provide a method and a system for analyzing and learning behavior based on an acquired stream of video frames. Objects depicted in the stream are determined based on an analysis of the video frames. Each object may have a corresponding search model used to track an object's motion frame-to-frame. Classes of the objects are determined and semantic representations of the objects are generated. The semantic representations are used to determine objects' behaviors and to learn about behaviors occurring in an environment depicted by the acquired video streams. This way, the system learns rapidly and in real-time normal and abnormal behaviors for any environment by analyzing movements or activities or absence of such in the environment and identifies and predicts abnormal and suspicious behavior based on what has been learned.

A machine-learning engine is disclosed that is configured to recognize and learn behaviors, as well as to identify and distinguish between normal and abnormal behavior within a scene, by analyzing movements and / or activities (or absence of such) over time. The machine-learning engine may be configured to evaluate a sequence of primitive events and associated kinematic data generated for an object depicted in a sequence of video frames and a related vector representation. The vector representation is generated from a primitive event symbol stream and a phase space symbol stream, and the streams describe actions of the objects depicted in the sequence of video frames.

An online learning behavior analysis-based individualized learning recommending method comprises the following steps of 1 collecting the data, and obtaining the learner log data, the student score data and the student character data of an online learning platform; 2 extracting and mining the features; 3 preparing a dataset to train a BP neural network and testing the accuracy of a model; 4 utilizing the trained model to predict the scores of the new learners; 5 issuing the questionnaires to the learners, and collecting the questionnaire data; 6 carrying out the k-means clustering analysis on the questionnaire data; 7 combining the character features of the different learners to recommend the individualized learning methods. The present invention provides a BP neural network prediction method that is higher in prediction precision and can analyze the online learning time regularity of the learners more comprehensively. According to the present invention, the characters of the learners can be analyzed, and the individualized learning methods can be recommended to the learners.

The invention relates to determination of a presentation pattern of a teaching material in a computer-assisted education system. An information processing apparatus includes first processor means for analyzing an answer from a user to determine a trait of the user related to a personality, and for determining a teaching material presentation pattern for the user in accordance with the trait of the user. Second processor means is provided for selecting and editing ones of a plurality of teaching material elements of a specific subject in accordance with the presentation pattern, generating a teaching material module, and presenting the teaching material module to the user in accordance with the presentation pattern. Third processor means is provided for analyzing learning behavior of the user during learning of the user using the presented teaching material module in accordance with the presentation pattern, and modifying the presentation pattern in accordance with the analysis.

Embodiments of the present invention provide a method and a system for analyzing and learning behavior based on an acquired stream of video frames. Objects depicted in the stream are determined based on an analysis of the video frames. Each object may have a corresponding search model used to track an object's motion frame-to-frame. Classes of the objects are determined and semantic representations of the objects are generated. The semantic representations are used to determine objects' behaviors and to learn about behaviors occurring in an environment depicted by the acquired video streams. This way, the system learns rapidly and in real-time normal and abnormal behaviors for any environment by analyzing movements or activities or absence of such in the environment and identifies and predicts abnormal and suspicious behavior based on what has been learned.

A machine-learning engine is disclosed that is configured to recognize and learn behaviors, as well as to identify and distinguish between normal and abnormal behavior within a scene, by analyzing movements and / or activities (or absence of such) over time. The machine-learning engine may be configured to evaluate a sequence of primitive events and associated kinematic data generated for an object depicted in a sequence of video frames and a related vector representation. The vector representation is generated from a primitive event symbol stream and a phase space symbol stream, and the streams describe actions of the objects depicted in the sequence of video frames.

The invention discloses an online teaching platform based on big data and a method for managing an online teaching process through such online teaching platform based on big data, belonging to the technical field of teaching management. The online teaching platform comprises a teaching system, a teaching affairs management system and a big data analysis system, the teaching system is in bidirectional connection with the teaching affairs management system through a HTTP, and is used for bidirectional synchronization of teaching affairs related data, such as students and course basic data and course selection information; the teaching system is connected with the big data analysis system, and is used for acquiring structured, semi-structured and non-structured data and storing the acquired data in the big data analysis system; and the big data analysis system is respectively connected with the teaching system and the teaching affair management system, and is used for feeding back learning behavior features and teaching quality analysis results to the teaching system and the teaching affairs management system. The platform and the method provided by the invention can provide full lifecircle learning and management services, such as student enrolling, online learning, teaching management and teaching quality evaluation, through an online one-stop mode, and can analyze and evaluatelearning and teaching quality and optimize learning experience.

A system and method to assist an instructor in managing, understanding, and drawing conclusions about the learning behavior. The system includes methods for processing data collected about users as they interact with the various modalities of learning that may be integrated into the course, and rendering this information into visualizations that are displayed on an instructor dashboard and updated in real time. This instructor interface includes a plurality of modules targeted towards the management of and interaction with users, and the analysis and visualization of a different aspect of their learning

The invention discloses a personalized test question recommendation method based on user learning behavior, the method is as follows: obtaining user history problem data, test questions and knowledgepoint information from an online education platform; constructing a user based on user history data data; R, according to the relationship between the test questions and the knowledge points, constructing the test questions-Knowledge point correlation matrix Q; constructing the user cognitive diagnosis model through the DINA model, obtaining the user knowledge point mastering matrix A; obtainingthe user adjacent test set according to the matrix A, according to Matrix Q the test questions-Adjacent test sets, constructing alternative test sets; non-negative matrix decomposition of matrix R, obtaining the implicit feature matrices W and H of users and test questions, find the estimated values of W and H matrices, and get the score prediction Model; calculating the potential answering situation of the user, and recommending the test questions of the target user's own difficulty range to the target user. The invention can accurately recommend the test questions suitable for the target user to the user. The invention is applicable to the field of online education.

The present technology may determine an anomaly in a portion of a distributed business application. Data can automatically be captured and analyzed for the portion of the application associated with the anomaly. By automatically capturing data for just the portion associated with the anomaly, the present technology reduces the resource and time requirements associated with other code-based solutions for monitoring transactions. A distributed business application is monitored to detect an anomaly based the learned behavior of the business application. The behavior may be learned for each of one or more machines which implement a distributed business transaction. The present system may automatically collect diagnostic data for one or more business transactions and / or requests based on learned behavior for the business transaction or request. Data for a distributed business application transaction may be collected by sampling one or more threads assigned to handle portions of the distributed business transaction. Data regarding the distributed transaction may then be reported from agents monitoring portions of the distributed transaction to one or more controllers and assembled by one or more controllers into business transactions.

The invention specifically relates to an individualized learning-oriented educational resource recommendation method and system. The method comprises the following steps of: S1, acquiring basic characteristic data of learners and establishing a resource interestingness model and a learning style model of the learners according to the basic characteristic data, wherein the basic characteristic data comprises identity information, learning behavior records and educational resource operation records of the learners; S2, establishing a learner characteristic model according to the resource interestingness model and the learning style model; S3, calculating the similarity between different learners or the similarity between different educational resources according to the learner characteristic model; and S4, generating a target learner-oriented educational resource recommendation result according to the similarity between different learners or the similarity between different educational resources. Through the technical scheme provided by the invention, the target learner-oriented education resource individualized recommendation can be realized.

The present invention discloses a learning behavior feature-based SPOC (small private online course) student weekly performance prediction method and device and belongs to the online learning field. Log data in the online courses of students are collected; the learning behavior features of the students are extracted from the log data so as to train a plurality of data mining models; and a data mining model of which the performance is optimal on a training set is adopted to predict the weekly performance of the students. Correspondingly, the prediction device of the present invention comprises a data acquisition module, a feature extraction module, a training data generation module and a prediction module. According to the learning behavior feature-based SPOC student weekly performance prediction method and device of the invention, compared with ordinary learning behavior features, the learning behavior features designed based on the learning habits of the students are integrated with the teaching experience of teachers, and therefore, the learning habits of the students in the online courses can be embodied, the improvement of the prediction accuracy of the prediction model can be facilitated, and the teachers can timely know students who have troubles in learning so as to adjust the difficulty of the courses and provide targeted counseling.

Vehicle behavior learning apparatuses, methods, and programs store pieces of feature information including position information and attribute information of a plurality of target features. The apparatuses, methods, and programs obtain vehicle position information that shows a current position of a vehicle, obtaining image information of surroundings of the vehicle, and obtain one of the stored pieces of feature information corresponding to the surroundings of the vehicle based on the vehicle position information. The apparatuses, methods, and programs perform an image recognition for recognizing a target feature contained in the image information that corresponds to the obtained piece of feature information, detect a behavior of the vehicle that is performed within a predetermined range from a position of the recognized target feature, and store, based on the vehicle position information, the detected behavior in correspondence with information of a position in which the detected behavior was detected. The apparatuses, methods, and programs extract, based the detected behavior being stored a plurality of times at the same position, the detected behavior as a learned behavior, output learned behavior information including attribute information of the detected behavior and the position information of the detected behavior, each of which are kept in correspondence with the piece of feature information of the recognized target feature.

The invention discloses a children somatic sense interactive learning system and a children somatic sense interactive learning method. The system comprises a somatic sense capturing and tracking assembly, a somatic sense interactive learning behavior intelligent processing module, a somatic sense behavior and leaning resource database, a personal computer (PC) host, an interactive display screen and a sound box; the somatic sense interactive learning behavior intelligent processing module and the somatic sense behavior and leaning resource database are integrated in the PC host; the PC host is connected with the somatic sense capturing and tracking assembly, the interactive display screen and the sound box; the PC host is connected with the somatic sense interactive learning behavior intelligent processing module, and the somatic sense behavior and leaning resource database in a two-way mode; the invention also discloses a method for learning by using the children somatic sense interactive learning system; and by the system and the method, children can perform interactive learning across the space through a terminal display screen in the most casual mode of a body.

The invention discloses a teaching quality evaluation system based on online education. The teaching quality evaluation system comprises a data loading module, an FLEX player, a data processing module and a data reporting module. According to the teaching quality evaluation system based on online education, learning cost coefficients of different corresponding courses, different chapters and different knowledge points are worked out through collection and statistics of learning behavior data of students; teaching quality evaluation of a teacher is achieved through analysis of all students. The data reporting module generates reporting information, and users can conveniently perform lateral comparison. The students can perform lateral comparison with other students and can know the learning phenomenon and the learning efficiency; on the basis of comparison of different courses, the students can know the courses which they are good at and the courses which they are not good at. On the basis of lateral comparison of different chapters, the teacher can know the chapters and the knowledge points which the students learn with difficulties and then improve the teaching quality.

The invention discloses a student modeling and personalized course recommendation method in an online learning system, and belongs to the education data mining field. According to the method, cognitive level modeling and personalized course recommendation of students are mainly studied, firstly, knowledge mastering states of the students are judged based on a cognitive diagnosis model, learning behaviors of the students are analyzed through data on a system platform, and then the cognitive abilities of the students are modeled by integrating course mastering conditions; secondly, an online course is modeled ; and finally, the features of the online course are fused according to the cognitive level of the student to perform personalized recommendation. According to the invention, personalized recommendation is carried out based on the cognitive level of students and in combination with the feature indexes of the online courses, so that the user can be helped to carry out more accurate personalized course recommendation, and the online course recommendation is more interpretable and acceptable.

The invention discloses a teaching system and method based on learning behavior analysis. The teaching system comprises a teaching server, a teacher terminal and student terminals. The teaching servercomprises a teaching resource server, a knowledge point test server, a question and answer interactive server, a learning behavior data acquisition server, and a learning behavior analysis server anda data visualization server. The learning behavior data acquisition server is used for collecting all kinds of behavior data produced caused by the student terminals visiting the teaching resource server, the knowledge point test server and the question and answer interactive server. The learning behavior analysis server classifies and analyzes all kinds of behavior data collected by the learningbehavior data acquisition server and displays the classification analysis results in the data visualization server. The teaching system and method based on learning behavior analysis acquires all kinds of learning behavior data of students in the learning process in real time, evaluates learning behavior data of each student by multi-dimensional quantification, and dynamically adjusts the teaching content according to the learning behavior analysis result, and a personalized teaching design for students is carried out.