Abstract: Methods and systems are described for novel uses and/or improvements to federated learning. As one example, methods and systems are described for improving the applicability of federated learning across various applications and increasing the efficiency of training a global model through federated learning. As another example, methods and systems are described for ensuring comprehensive training data is available to models assigned by the federated learning server. Additionally, methods and systems are described for improving the rate of training a global model through federated learning.

Abstract: A facility for providing an XR device-based tool for cross-platform content creation and display is disclosed. The facility causes a definition of a mixed reality (MR) procedure to be presented by an editing MR device in a virtual application window. Input is received via the editing MR device. The facility interprets the received input as one or more editing actions performed with respect to the MR procedure definition and alters the MR procedure definition accordingly. The facility creates a device-independent representation of the MR procedure based on the altered MR procedure definition.

Abstract: The facility provides an environment for creating a mixed reality (MR) application. Input specifying a property of a networked device is to be accessible in the mixed reality application is received. Properties of one or more networked devices are identified. A property selection interface is provided in the environment for creating the mixed reality application. The facility receives, via the property selection interface, selection of a networked device of the one or more networked devices and a property of the networked device. The facility configures the MR application to provide, to a device executing the MR application, access to the selected property of the selected networked device. The MR device can execute the MR application to access the selected property of the selected networked device.

Abstract: The systems and methods use a divergent artificial intelligence architecture. To summarize contextually relevant information on user-selected concepts despite the lack of adequate training data on each user, the system may use an architecture in which a first model is trained to map a plurality of user interactions to inputted concept identifiers to determine user relationship matrices whereas a second model is trained to generate contextually relevant summaries on inputted concepts and inputted user relationship matrices. For example, by using this divergent structure, the system does not need to be specifically trained to generate contextually relevant summaries based on each individual user's relationship to a given concept.

Abstract: A portion of a source mixed reality (MR) experience is retrieved. Then, the portion of the source MR experience is used to generate a serialized representation including a hierarchy of tagged elements. The hierarchy of tagged elements includes a plurality of MR step elements collectively defining a procedure to be performed by a viewer of an MR experience. Each MR step element has child elements that include an MR step number indicating a position of the MR step in the MR procedure and an MR step ID element indicating an identity of the MR step. The serialized representation is deserialized to generate a portion of a target MR experience to be edited in an MR development tool. The portion of the target MR experience is usable to cause each MR step in the plurality of MR steps to be graphically represented in the MR development tool.

Abstract: Methods and systems are described herein for protecting client data while training machine learning models. The system may transmit, to client devices, simple models to be trained on a respective client device to generate predictions based on a respective subset of respective client data of the respective client device. The system may receive the trained simple models from the client devices. The system may input, into an ensemble model including the simple models, an unlabeled synthetic dataset. This may cause the ensemble model to aggregate a set of predictions generated by each simple model to generate labels for the unlabeled synthetic dataset. The system may then input, into a new model, the unlabeled synthetic dataset and the labels to train the new model to predict the labels for the unlabeled synthetic dataset.

Abstract: Described herein are systems, methods, and programming for facilitating user-specific data transfers to provide task-related context to a user. In response to receiving a request to execute a computing task, one or more artificial intelligence models may generate a representation of the request encoding information about the request. The artificial intelligence models may identify another representation of another request that is similar to the generated representation. The similarity may indicate that another user previously submitted a request to execute a computing task that is similar to the requested computing task. This other representation may be selected and provided to a requesting user's device with a data transfer program configured to cause a decoder implemented by the requested user's device to extract the information encoded by the provided representation. The requesting user can use the extracted information to execute the computing task.

Abstract: A processor of a card may determine a predetermined voltage associated with the card. The processor may receive an indication of a voltage received from a terminal, wherein the card is powered by the voltage received from the terminal. The processor may determine that the voltage received from the terminal is not equal to the predetermined voltage associated with the card. The processor may reject, based on the determination that the voltage received from the terminal is not equal to the predetermined voltage associated with the card, a request from the terminal to provide payment information associated with the card.

Abstract: Systems and methods are disclosed herein for generating updated descriptions of items based on analyzing candidate embeddings of semantic representations of item descriptions. The system may obtain a text file describing an item. The system may provide the text file to a generative language model to generate semantic representations of the text file. The system may generate, based on the text file, candidate embeddings in an embedding space. The system may obtain embeddings associated with existing items. The system may determine subsets of the embeddings within a threshold distance. The system may compare the subsets. The system may determine attributes associated with a candidate embedding based on the comparison. The system may generate an updated text file based on the attributes.

Abstract: Systems and methods for triggering token alerts. In some aspects, the system, after determining that the probability that an authentication request from an authentication token is associated with a malicious activity is above a threshold, determines whether a user device associated with the authentication token is within a threshold distance of the authentication token. In response to determining that the authentication token is not within the threshold distance of the user device, the system declines the authentication request and transmits an alert request to the authentication token to emit an audio signal from a speaker included in the authentication token.

Abstract: Methods and systems are described herein for protecting client data while training machine learning models. The system may transmit, to client devices, simple models to be trained on a respective client device to generate predictions based on a respective subset of respective client data of the respective client device. The system may receive the trained simple models from the client devices. The system may input, into an ensemble model including the simple models, an unlabeled synthetic dataset. This may cause the ensemble model to aggregate a set of predictions generated by each simple model to generate labels for the unlabeled synthetic dataset. The system may then input, into a new model, the unlabeled synthetic dataset and the labels to train the new model to predict the labels for the unlabeled synthetic dataset.

Abstract: Systems and methods disclosed may include receiving verification information from a user via a graphical user interface (GUI) and capturing visual information of the user via at least one imaging sensor. A viewing angle of the user may be continuously determined and based on the captured visual information. Upon determining that the verification information is valid, the method may include causing a display to output the account information and other information where the account information is displayed at the continuously determined viewing angle of the user, and the other information may be displayed at one or more viewing angles other than the continuously determined viewing angle of the user.

Abstract: A footwear component is disclosed. In various embodiments, the footwear component includes a foam substrate having an upper surface and a lower surface extending in a lateral direction; and a sensing circuit between the upper surface and the lower surface, the sensing circuit including a piezoelectric foam sensor including a piezoelectric foam between a plurality of electrodes, a printed circuit board having a processor electrically connected to the plurality of electrodes by one or more electrical leads, and an antenna electrically connected to the printed circuit board, the antenna being spaced apart from the printed circuit board and the plurality of electrodes in the lateral direction.

Abstract: Systems and methods for visual manipulation and execution of machine learning models rendered in a three-dimensional space. In some aspects, the system receives configuration data representing a machine learning model and generates a three-dimensional representation of the machine learning model by (1) generating virtual objects corresponding to nodes and edges of the model and (2) configuring values of virtual object parameters for virtual objects based on associated weight matrices from the configuration data. The system detects a user gesture that indicates a command to perform a modification of the machine learning model and, responsive to detecting the user gesture, causes execution of a modified machine learning model. The system generates a new three-dimensional representation of the modified machine learning model.

Abstract: Methods and systems for scalable dataset content embedding for improved searchability. For example, the system may retrieve a first dataset from a first data source. The system may generate a first data profile of the first dataset. The system may generate a latent index of the first data profile based on processing the first data profile using a first embedding algorithm. The system may receive, via a user interface, a first request for a first text string. The system may generate an embedded request corresponding to the first request based on processing the first text string using the first embedding algorithm. The system may process the embedded request using the latent index. The system may generate for display, in the user interface, a result based on processing the embedded request using the latent index.

Abstract: A facility for providing an XR device-based tool for cross-platform content creation and display is disclosed. The facility causes a definition of a mixed reality (MR) procedure to be presented by an editing MR device in a virtual application window. Input is received via the editing MR device. The facility interprets the received input as one or more editing actions performed with respect to the MR procedure definition and alters the MR procedure definition accordingly. The facility creates a device-independent representation of the MR procedure based on the altered MR procedure definition.

Abstract: In some implementations, a verification device may receive a request for an electronic exchange of information. The verification device may receive geographic location information associated with the request for the electronic exchange of information. The verification device may determine, using a machine learning model and based on the geographic location information, a validity of the request for the electronic exchange of information. The verification device may determine whether to execute the electronic exchange of information based on the determination of the validity of the request for the electronic exchange of information. The verification device may perform one of executing the electronic exchange of information or rejecting the request for the electronic exchange of information based on the determination of whether to execute the electronic exchange of information.

Abstract: Systems and methods for generating synthetic data samples. In some aspects, a system accesses first and second user data samples corresponding to a first and second period of time, respectively, and generates a first and second profile based on the user data samples, wherein the profiles comprise parameters representing metadata associated with corresponding user data samples. The system generates a new profile corresponding to an intermediary period of time between a first and second period of time, wherein the new profile comprises intra-profile and inter-profile parameters. The system determines (1) a value for each intra-profile parameter based on values of intra-profile parameters of the first and second profile and (2) the value for each inter-profile parameter based on a predicted value output of a model trained on a plurality of data profiles over time and generates synthetic data samples based on values of the inter-profile and intra-profile parameters.