37 results about "Shared learning" patented technology

In various embodiments, the present invention is directed to a decentralized and secure method for developing machine learning models using homomorphic encryption and blockchain smart contracts technology to realize a secure, decentralized system and privacy-preserving computing system incentivizes the sharing of private data or at least the sharing of resultant machine learning models from the analysis of private data. In various embodiments, the method uses a homomorphic encryption (HE)-based encryption interface designed to ensure the security and the privacy-preservation of the shared learning models, while minimizing the computation overhead for performing calculation on the encrypted domain and, at the same time, ensuring the accuracy of the quantitative verifications obtained by the verification contributors in the cipherspace.

The invention relates to a method for quickly classifying and retrieving sentences in an article by using an electronic device. The electronic device generates a classified catalog listing of the sentences in the article according to a specified classification method; when in retrieval: a processor extracts the sentences in the content of an e-book opened by the user one by one, searches the classified catalog in which the sentence is positioned, and adds the classified catalog name to the sentence as a classification mark annotation; and after the sentence with the classification mark is selected by the user, a sentence reading pointer is positioned in the classified catalog, and the sentences therein are output. The method provides a brand-new learning mode for people to read excellent classic articles. The user can annotate the contents in which the user is interested in the learning process, and build an own reading notes database of the user. The reader also can modify and improve a ready-made material database according to own preference, learns by sharing with others through the Internet, and improves the reading and learning efficiency in cooperative learning.

The invention relates to the technical field of machine learning, and aims to provide a neural network model training method based on shared learning. According to the method, a plurality of shared learning participants perform hybrid security multi-party calculation and central node local calculation through secret sharing to realize neural network model training; the method specifically comprises the steps of performing data preparation; loading and initializing data; loading the current training batch; calculating a first-layer linear output; calculating a model prediction value; calculating a model output gradient; updating the model and calculating; updating the local sharing weight; performing assessment on a test set. According to the method, multi-party security calculation and local calculation are combined, and the shared learning efficiency is improved. Through an online encryption multiplication triad provider, the efficiency of secure multi-party calculation of secret sharing is improved, and the overhead is greatly reduced compared with schemes of homomorphic encryption, confusion circuits and the like. The method is suitable for a longitudinal shared learning scene,and can enable all parties to carry out federated modeling under the condition that no overlapping features exist.

The invention discloses an evacuation simulation method and system based on multi-agent shared Q learning, wherein an evacuation scene is bulit, a crowd evacuation doble-layer control mechanism is built, the upper space is composed of management agent, navigation agent and knowledge base, and the lower space is pedestrian to be evacuated; the crowd is grouped, each group selects the leader, establishes the association between the group and the navigation agent, and the leader of each group corresponds to a navigation agent; each navigation agent puts real-time information into the bulletin board managed by management agent in the process of evacuating each group; multi-Agent adopts shared Q learning algorithm to learn, and the result of shared learning is sent to navigation Agent in real time, so that navigation Agent can plan the route according to the global evacuation situation. Each group evacuates to the evacuation exit according to the guidance of the corresponding navigation agent.

The invention mainly belongs to the technical field of multi-target optimization, and particularly relates to a novel multi-target particle swarm optimization method. The method is based on shared learning and Cauthy mutation. Shared learning factors are adopted in the method to change the speed and position updating formula of particles, and the optimal position of the particle individuals and external files are updated by adopting Cauthy mutation operators. According to the method, the global search capability and the local optimization search precision of the particles can be enhanced, and the algorithm is enabled to fast approach Pareto leading edge and premature convergence of the algorithm can be avoided. According to the method, solution convergence, diversity and distributivity of the multi-target particle swarm algorithm for processing the problem of multi-target optimization can be enhanced.

The invention provides a robot shared learning system based on a cloud platform. The system comprises a private model generation terminal and a cloud fusion computing sharing platform. The private model generation terminal is used for uploading the locally generated private model and the environmental feature information collected for generating the private model to the cloud fusion computing sharing platform. The cloud fusion computing sharing platform comprises a model fusion computing module. The model fusion calculation module is used for combinding the uploaded private model with other robot terminals, a private model generation terminal and / or other private models to generate uploaded environment feature information and merging and calculating the information with a sharing model stored on a cloud sharing computation sharing platform to generate a novel sharing model. The novel sharing model is downloaded and / or studied by other robot terminals, the private model generation modeland / or other private model generation models.

The invention discloses a network education shared learning system comprising a login module client, a cloud server and a big data analysis module, wherein the cloud server is in communication connection to a memory; the login module client, a course structure classification module and a upload module are used as data transmission ends to be connected to the cloud server; an excellent lecture module is connected to the upload module; the cloud server is also in communication connection with an online detection module, a download module, an answering module and a voice mutual assistance module;the answering module is connected to a score accumulation calculation module, a management evaluation module, and the big data analysis module. The network education shared learning system applies acurrent chat mode to the learning sharing system, has a mutual assistance effect, and introduces the big data analysis module to analyze the defect of each student according to the login information of each student to realize targeted learning.

The invention discloses an internet learning aid system based on big data. The internet learning aid system comprises a user terminal and a cloud server, wherein the cloud server comprises a user registration module, a plan formulation module, a shared learning module, a learning progress display module, a learning reminding module and a big data analysis module. The internet learning aid system comprises the learning reminding module, the learning reminding module can directly remind a user of learning by virtue of WeChat or a cell-phone number, and lots of WeChat and mobile phones used by modern people are combined, so that the user can be effectively reminded of learning. According to the system disclosed by the invention, the learning condition of the user can be analyzed by virtue ofthe big data and fed back to the user by virtue of a learning report, so that a good learning habit of the user is formed. The system disclosed by the invention can help the user to formulate a plan and provide online learning plan management for the user. Moreover, due to the shared learning module, personnel learning the same type at the same time can be selected to perform shared learning, so that a co-learning atmosphere is created for users.

The invention mainly belongs to the technical field of multi-objective optimization, and specifically relates to a novel quantum particle multi-objective optimization method. The novel quantum particle multi-objective optimization method is used to improve the accuracy, diversity and evenness of solutions for handling the problem of multi-objective optimization using a quantum particle swarm algorithm. The novel quantum particle multi-objective optimization method comprises the steps of establishing a quantum double-potential-well model based on double-delta potential well simplification, establishing a particle location updating model based on the double-potential-well model, and constructing a shared learning strategy of particles. The local optimization precision of the algorithm is improved, and solutions are distributed more uniformly. The shared learning mechanism is used to widen the search scope of particles and increase the diversity of solutions, and the tendency of the existing quantum particle swarm algorithm to easily converge to a boundary solution is avoided. Good convergence performance and distribution performance can still be maintained during handling of the problem of high-dimension target optimization. A novel practical method is provided for solving the problem of multi-objective optimization in engineering application.

Under the present invention a hierarchical tree corresponding to the learning environment is provided. When the state of a shared learning competency in the learning environment is changed, branched rollup through the hierarchical tree is performed so that all applicable nodes are rolled up without a node being rolled up more than once. Specifically, when the state of the shared learning competency is changed, control blocks are generated for each predecessor of the “sharing” nodes. The control block for each predecessor identifies the successors (nodes) that must be rolled up before the predecessor itself can be rolled up. Under this methodology, a node will only be rolled up when all necessary successors have been rolled up, thus, preventing repeated rollup of any of the nodes.

The invention relates to a data processing technology, and aims to provide a multi-source multi-modal data preprocessing method and system for shared learning. The method comprises the following steps: performing data communication across servers; encrypting multi-source data; aligning privacy protection samples; and filtering the multi-source data to form final unified data. According to the invention, a preprocessing stage before training of the shared learning model is abstracted into a system, and a preprocessing process is completed step by step by utilizing a plurality of subsystems, sothat cross-server data communication, multi-source data encryption, privacy protection sample alignment and multi-source data filtering are realized, the shared learning system preprocessing stage issystematic and more specific and complete, the multi-source data communication, encryption, alignment and filtering method is provided, the execution efficiency is higher, the security is higher, andthe data privacy can be ensured; and the method is independent of a specific shared learning task, and can be integrated in any multi-source data processing task as a single module.

The invention provides a controlled shared learning method and system based on federated learning, and the method comprises the steps: receiving the parameters of all client models Vi,t, selecting oneor more shared clients from all clients according to the parameters, and transmitting a sharing instruction to a shared client, wherein Vi,0 is obtained through calculation power construction of eachclient and training according to a corresponding private data set, and classification module structures of all the models Vi,0 are consistent; and obtaining a classification module parameter St of the shared client model Vi,t, and sending the calculated classification module parameter St + 1 to each client, so that each client obtains the model Vi,t + 1 according to the private data set and the classification module parameter St + 1. According to the method, only part of model parameters need to be provided to complete shared learning, so that the data security is well guaranteed; and whethereach client model participates in shared learning and the like is autonomously controllable.

The invention discloses a creation sharing and learning management system and method. The system comprises a data input generation module used for inputting staff information and automatically generating staff login passwords and credit points needed to be finished by staffs, a login module used for login, a video uploading permission management module used for submitting video uploading permission application data, performing auditing, and if the auditing is passed, obtaining a video uploading permission, or otherwise, not obtaining the video uploading permission, a video recording and uploading module used for recording and uploading a learning video, a storage module used for storing the uploaded learning video, and a video management module used for auditing the uploaded learning video, and if the auditing is passed, releasing the learning video, or otherwise, not releasing the learning video. Knowledge of front-line leaders and management elites in a company can be recorded through a mobile video technology and is shared and learnt with the staffs in the company through a platform, so that very precious knowledge wealth is accumulated for an enterprise and the training education cost is reduced.

The invention relates to the technical field of network equipment application, in particular to a network education shared learning system which comprises a base, an insertion rod, a protective shell,a tablet personal computer body and a fixing mechanism. The insertion rod is arranged at the top of the base. The protective shell is fixedly connected to the top of the insertion rod, and the tabletpersonal computer body is arranged in the protective shell. A cavity is formed in the insertion rod. The fixing mechanism is arranged in the cavity. The fixing mechanism comprises an electric push rod, a first push plate, a first air bag, an air guide pipe, a shell, a second push plate, a push rod and a pressing plate. The electric push rod is arranged at the top of the cavity. The first push plate is arranged at the output end of the electric push rod. The shell is fixedly connected to the bottom of the cavity. The first air bag is arranged at the top of the shell. The air guide pipe is arranged on the side wall of the first air bag, and the first air bag communicates with the shell through the air guide pipe. A tablet personal computer can be stably mounted at the top of the base.

The invention discloses an online sharing system for English teaching. The system comprises an identity management module used for recording the information of a user, transmitting a random login password to a mobile terminal of the user, verifying whether the random login password is correct or not, and allowing login if the random login password is correct; a face recognition module used for recognizing face information of the user and recognizing the identity and authority of the user according to the face information; an authority management module used for managing the authority of the user according to the identity of the user; a creation module used for creating shared learning content; a knowledge base management module used for storing and auditing learning contents; and a sharingmanagement module used for each user to communicate in the learning process. The English teaching online sharing system disclosed by the invention realizes online sharing of English teaching, and isvery convenient.

The invention provides a model processing method for shared learning. The method comprises the following steps: obtaining a plurality of candidate model update values for an initial neural network model; performing quality evaluation on the plurality of candidate model update values to obtain weights of the plurality of candidate model update values; adopting a median mechanism with weight for the plurality of candidate model update values, and determining a model update value for updating the initial neural network model; and updating the initial neural network model based on the determined update value. By firstly obtaining the plurality of candidate model update values, then obtaining weights of the plurality of candidate model update values through quality evaluation, and then determining the model update value for updating the initial neural network model based on the weighted median mechanism, based on the weighted median mechanism, it can be avoided that a false trained neural network model is adopted to update the initial neural network model, and the problem that an updated neural network model does not conform to a real situation by adopting an existing model updating method can be solved.

The invention discloses a campus mutual assistance sharing platform based on a WeChat applet and an operation method thereof. The platform comprises a front-end management system and a background database, the front-end management system comprises a home page unit, a message unit and a personal data unit. The home page unit comprises a home page display page and a learning sharing sub-page arranged on the home page display page; the learning sharing sub-page comprises a question and answer module, a resource sharing module, a class rubbing module and a science and technology frontier module; wherein the question and answer module is used for asking questions and answering questions among users, the resource sharing module is used for sharing learning and living resources among the users, the class rubbing module is used for the users to query school course information, the science and technology frontier module is used for the users to browse school scientific research information, andthe background database is used for storing the school course information and personal information of the users. According to the invention, full utilization of platform resources can be promoted, relevance and interactivity among students in schools are improved, the learning atmosphere of students in schools is aroused, and the communication circle of students is expanded.

The invention discloses a low-illumination pedestrian detection method and system based on multi-task feature fusion shared learning. The method comprises the steps: acquiring normal and low-illumination pedestrian data sets; pre-training an image illumination enhancement network by using the normal and low-illumination pedestrian data sets; pre-training a pedestrian detection network by using thenormal illumination pedestrian data set; designing a multi-task feature fusion module capable of fusing features between upstream and downstream tasks, performing feature fusion and sharing on the two networks, and constructing a low-illumination pedestrian detection network based on multi-task feature fusion shared learning; importing the two pre-training models into the low-illumination pedestrian detection network, and performing training by utilizing the normal and low-illumination data sets to obtain a low-illumination pedestrian detection model based on multi-task feature fusion sharedlearning; and detecting a detected image by using the low-illumination pedestrian detection model based on multi-task feature fusion shared learning to obtain the position of a pedestrian in the image. According to the method, the position of the pedestrian can be accurately and efficiently detected in the low-illumination image.

The invention discloses a language learning machine system based on a sharing mode. The language learning machine system comprises a language learning machine, a sharing site, a background server anda user mobile phone, wherein the language learning machine is in communication connection with the sharing site, the language learning machine is in communication connection with the background server, the user mobile phone is in communication connection with the sharing site, and the user mobile phone is in communication connection with the background server. The system can be used for shared learning, and the structure of the sharing system is simplified; the system is simple, the cost is reduced, and the learning efficiency of users is improved.

Disclosed are a smart learning system using a device social relation and a method thereof. The smart learning system includes a first device that generates a device social relation and shares learning content, and at least one second device that executes an event related to the learning content by acquiring the shared learning content when is included in the device social relation.

The invention provides an interactive information-based teaching system, which includes a teaching terminal, teaching equipment, interactive media, a learning terminal, a server, and a communication network; After linking, you can add, modify, and share teaching resources. The learning terminal obtains teaching resources through the communication network, interactive media and server connection to learn knowledge, discuss and complete a certain learning project, and submit shared learning results. The interactive media is located on the specific teaching equipment. The location and provide specific teaching resources to the learning end for students' personalized learning or simulation exercises; teaching resources are a collection of various teaching resources that can be edited, added, modified, commented and shared. The invention provides a good interactive information teaching system for practical training teaching and "integrated teaching of theory and practice".

The invention discloses a quantum particle swarm multi-objective optimization method. The method comprises the following steps: S1, establishing a quantum double-delta potential well model based on double-potential well simplification; S2, establishing a particle position updating model based on the double potential well model; S3, constructing a shared learning strategy of the particles; And S4,constructing a quantum particle swarm multi-objective optimization algorithm. According to the quantum double-potential well model, a particle position updating formula is established, an inner localattractor and an outer local attractor are introduced, the local optimization precision of the algorithm is improved, and solution distribution is more uniform; According to the shared learning mechanism provided by the invention, the searching range of particles can be expanded, the diversity of solutions is increased, and the tendency that a quantum particle swarm algorithm is easy to converge to a boundary solution is avoided; When the method is used for processing the high-dimensional target optimization problem, the good convergence performance and distribution performance can still be kept, and the multi-target optimization problem in engineering application can be solved more practically.

The invention particularly relates to a shared learning device, and belongs to the technical field of learning devices. The shared learning device comprises a device body, and further comprises a control system module and a scanning acquisition module which are arranged inside the device body, wherein the control system module is connected with the scanning acquisition module; a display and lightsource acquisition module is arranged on one side of the device body, mutually-symmetrical supports are arranged on the two sides of the display and light source acquisition module of the device body,the mutually-symmetrical supports are connected with a first extending turnover support and a second extending turnover support correspondingly, and the upper end of the first extending turnover support and the upper end of the second extending turnover support are connected with a turnover mechanism; the turnover mechanism is sleeved with a turnover LED lamp, and a first horn and a second horn are arranged at the two outer sides of the device body; a page scanning port is formed in the upper surface of the side, away from the display and light source acquisition module, of the device body; and a page inlet and a page outlet are formed in the two outer sides, intersecting with the device body, of the vertical horizontal plane of the page scanning port.

The invention belongs to the technical field of intelligent learning equipment, and particularly relates to a portable intelligent shared learning machine. The machine comprises a voice receiving module, a one-touch calling module, a micro-processing module, a built-in storage module, a sharing platform, a communication module, a voice output module and a display module. According to the portableintelligent shared learning machine, rich learning resources on the sharing platform can be obtained, the requirements of people learning from foreign teachers are met, and and intelligence is high. On the basis of the basic function of an existing learning machine, the function of calling the foreign teachers through one touch is set; by arranging the one-touch calling module, the user can send akey signal to the one-touch calling module when needing to practice oral conversation with the foreign teachers, and send out a connection request to a mobile terminal or a learning machine terminalof the foreign teachers stored in the corresponding built-in storage module through the communication module, so that communication between user and the foreign teachers is achieved, convenience is brought to connection, and the user can connect with the foreign teachers conveniently.

The invention relates to a speech emotion recognition method and device based on meta-multi-task learning. The method comprises the following steps: by combining meta-learning and multi-task learning, learning the correlation between auxiliary tasks and learning the knowledge migration capability from the auxiliary tasks to a main task. The method has the main advantages that: for speech emotion recognition, the correlation of emotion in a continuous space and a discrete space is considered, on a support set, meta learning can learn correlation of auxiliary tasks like multi-task learning, and multi-task learning can share a learning device like element learning. On a query set, a knowledge migration mechanism is introduced, so that the model can model the correlation between the main task and the auxiliary task. The device comprises an acquisition unit, a data processing unit, a metadata generation unit, an initialization unit, a meta-training unit, a meta-prediction fine tuning unit and a meta-prediction identification unit. According to the invention, the accuracy of speech emotion recognition is remarkably improved.

In various embodiments, the present invention is directed to a decentralized and secure method for developing machine learning models using homomorphic encryption and blockchain smart contracts technology to realize a secure, decentralized system and privacy-preserving computing system incentivizes the sharing of private data or at least the sharing of resultant machine learning models from the analysis of private data. In various embodiments, the method uses a homomorphic encryption (HE)-based encryption interface designed to ensure the security and the privacy-preservation of the shared learning models, while minimizing the computation overhead for performing calculation on the encrypted domain and, at the same time, ensuring the accuracy of the quantitative verifications obtained by the verification contributors in the cipherspace.

The invention discloses an evacuation simulation method and system based on multi-agent shared Q learning, wherein an evacuation scene is bulit, a crowd evacuation doble-layer control mechanism is built, the upper space is composed of management agent, navigation agent and knowledge base, and the lower space is pedestrian to be evacuated; the crowd is grouped, each group selects the leader, establishes the association between the group and the navigation agent, and the leader of each group corresponds to a navigation agent; each navigation agent puts real-time information into the bulletin board managed by management agent in the process of evacuating each group; multi-Agent adopts shared Q learning algorithm to learn, and the result of shared learning is sent to navigation Agent in real time, so that navigation Agent can plan the route according to the global evacuation situation. Each group evacuates to the evacuation exit according to the guidance of the corresponding navigation agent.