Abstract: The present disclosure relates to a method and a system for authenticating a virtual representation of a user in a virtual environment. The method may include receiving an authentication request to authenticate the virtual representation of the first user in the virtual environment; based on the virtual representation of the first user being an authenticated virtual representation of the first user, extracting one or more authentication parameters embedded within the virtual representation of the first user; and displaying, in the virtual environment, authentication information of the virtual representation of the first user based on the one or more authentication parameters.

Abstract: A method for providing an adaptive user interface (UI) navigation on a user equipment (UE) using a machine learning (ML) model is provided.

Abstract: Various embodiments of the present invention provide methods, apparatuses, computing devices, and/or the like that are configured to perform software application framework monitoring using alert signatures for the software applications that are generated by at least one of: (i) a conditional ensemble machine learning framework comprise one or more alert priority score generation machine learning models, one or more alert priority explanation generation machine learning models, and a conditional ensemble machine learning model that is configured to generate an explanation-inclusive alert signature if a deep-learning-based alert priority score generated by the alert priority score generation machine learning models is identical to a decision-tree-based alert priority designation generated by the alert priority explanation generation machine learning models, and (ii) a set of alert priority score adjustment models such as an entity-based alert priority score adjustment model, a temporal alert priority score adju

Abstract: A method for preventing data leakage may include: identifying data that is generated by at least one framework application in response to a data request from a first machine learning (ML) engine of a plurality of ML engines; creating a plurality of data blocks based on the generated data, a category of the first ML engine, and a tag associated with the first ML engine and the at least one framework application; determining whether the plurality of data blocks are valid to share with the first ML engine using an activity block chain associated with each of the plurality of framework applications; based on the plurality of data blocks being valid, sharing the plurality of data blocks with the first ML engine, and otherwise discarding the plurality of data blocks not to share with the first ML engine.

Abstract: A method, an electronic device and a non-transitory computer readable medium for securing sensor data are provided. The method comprises: obtaining context information by fetching a plurality of sensor data by a plurality of applications, mapping the plurality of sensor data to the plurality of applications and storing the mapped plurality of sensor data, generating pre-processed information based on the context information and the mapped plurality of sensor data, creating an inference category of the pre-processed information based on the pre-processed information and information from a database, and predicting at least one useful inference and at least one harmful inference of the plurality of sensor data based on the inference category, the context information and the plurality of sensor data.

Abstract: Disclosed are various embodiments for accelerating transactions and managing cashflow by predicting and crediting payment amounts for requests for payments that are satisfied by third party providers (e.g., insurance providers, claim payment providers, etc.). A user who is a registered participant in an accelerated transaction program can submit a claim to a claim payment provider requesting payment or reimbursement of funds associated with an event. While waiting for the claim payment provider to process the claim, trained models can be used to analyze the claim data, user data, claim payment provider data, historical data, and/or other information to (1) predict whether the claim will be approved by the claim payment provider and (2) estimate an amount that will be provided to the medical provider by claim payment provider. The user can be credited for the predicted amount while waiting for the claim to be processed.

Abstract: A method of applying secrecy settings on a user device is provided. The method includes monitoring, by the user device, user activity data based on usage of one or more applications of the user device, determining, by the user device, whether a user is exhibiting a secretive behavior based on the user activity data and historical behavior of the user, extracting, by the user device, contextual information from other applications, determining, by the user device, whether the contextual information is related to the user activity data, processing, by the user device based on the secretive behavior, the contextual information to provide at least one data stream and at least one attribute associated with the contextual information, determining, by the user device, a predefined secrecy type based on an analysis of the at least one data stream and the at least one attribute, and applying, by the user device, secrecy settings on the user device based on the predefined secrecy type.

Abstract: Various embodiments of the present invention provide methods, apparatuses, computing devices, and/or the like that are configured to perform software application framework monitoring using alert signatures for the software applications that are generated by at least one of: (i) a conditional ensemble machine learning framework comprise one or more alert priority score generation machine learning models, one or more alert priority explanation generation machine learning models, and a conditional ensemble machine learning model that is configured to generate an explanation-inclusive alert signature if a deep-learning-based alert priority score generated by the alert priority score generation machine learning models is identical to a decision-tree-based alert priority designation generated by the alert priority explanation generation machine learning models, and (ii) a set of alert priority score adjustment models such as an entity-based alert priority score adjustment model, a temporal alert priority score adju

Abstract: Various embodiments of the present invention provide methods, apparatuses, systems, computing devices, computing entities, and/or the like for facilitating efficient and effective execution of database management operations. For example, various embodiments of the present invention describe techniques for enabling access to anatomized data associated with a database table without using computationally expensive data anatomization techniques that use data masking and/or data manipulation and by using limited access pointers that are generated using randomized associations between database rows of a database table.

Abstract: Methods and systems for the transfer of samples collected on swabs (or similar sample collectors) onto particular types of substrates for subsequent analysis or other processing. The method involves contacting the sample collector containing a sample including the charged substance with the electrophoresis matrix; applying an electric field across the electrophoresis matrix to effect transfer of the charged substance from the sample collector to the electrophoresis matrix. The useful systems may assist in the performance of an operation on a charged substances within an electrophoresis system. Such systems may include a first electrolyte reservoir including a first electrode; a second electrolyte reservoir including a second electrode; a thread extending between the first and second electrolyte reservoirs; a third reservoir through which the thread passes, and a controller for controlling the application of an electric field across the thread so as to effect movement of a charged substance along the thread.

Abstract: Systems and methods for determining the priority of a set of reports available to a user. The method comprising determining, by a reporting application, a set of reports available to a user, each report including one or more metrics. The method further comprising obtaining metric data associated with each of the set of reports available to the user, accessing one or more priority calculation parameters for each report in the set of reports, calculating a priority value of each report of the set of reports based on the metric data and the priority calculation parameters for each report, and determining one or more high priority reports based on the priority value of each report of the set of reports.

Abstract: The present disclosure provides a method for intelligent user localization in a metaverse, including: detecting movements of a wearable head gear configured to present virtual content to a user, and generating sensor data and visual data using an inertial sensor and a camera, respectively, mapping the visual data to a virtual world using an image associated with the visual data to localize the user in the virtual world; providing the visual data and the sensor data to a first Machine Learning (ML) model and a second ML model, respectively; extracting a plurality of key points from the visual data and distinguishing stable key points and dynamic key points; and removing visual impacts corresponding to the visual data having a relatively low weightage, and providing a relatively high weightage to the sensor data processed through the second ML model.

Abstract: Various embodiments of the present invention provide methods, apparatuses, systems, computing devices, computing entities, and/or the like for facilitating efficient and effective execution of database management operations. For example, various embodiments of the present invention describe techniques for enabling access to anatomized data associated with a database table without using computationally expensive data anatomization techniques that use data masking and/or data manipulation and by using limited access pointers that are generated using randomized associations between database rows of a database table.

Abstract: This disclosure is directed to methods, computer program products, and systems for providing surface vehicle tracking data, including indications of potential collision zones, to an airport map display system onboard an aircraft. In one example, a method includes identifying historical navigation route data, aerodrome guidance features, and a predicted path of a first vehicle. The method further includes determining predicted positions along the predicted path and determining predicted positions of a second vehicle and comparing vehicle envelopes for the two vehicles to determine a predicted collision zone of the vehicles.

Abstract: Various embodiments of the present invention provide methods, apparatuses, computing devices, and/or the like that are configured to perform software application framework monitoring using alert signatures for the software applications that are generated by at least one of: (i) a conditional ensemble machine learning framework comprise one or more alert priority score generation machine learning models, one or more alert priority explanation generation machine learning models, and a conditional ensemble machine learning model that is configured to generate an explanation-inclusive alert signature if a deep-learning-based alert priority score generated by the alert priority score generation machine learning models is identical to a decision-tree-based alert priority designation generated by the alert priority explanation generation machine learning models, and (ii) a set of alert priority score adjustment models such as an entity-based alert priority score adjustment model, a temporal alert priority score adju

Abstract: Various embodiments of the present invention provide methods, apparatuses, computing devices, and/or the like that are configured to perform software application framework monitoring using alert signatures for the software applications that are generated by at least one of: (i) a conditional ensemble machine learning framework comprise one or more alert priority score generation machine learning models, one or more alert priority explanation generation machine learning models, and a conditional ensemble machine learning model that is configured to generate an explanation-inclusive alert signature if a deep-learning-based alert priority score generated by the alert priority score generation machine learning models is identical to a decision-tree-based alert priority designation generated by the alert priority explanation generation machine learning models, and (ii) a set of alert priority score adjustment models such as an entity-based alert priority score adjustment model, a temporal alert priority score adju

Abstract: Provided is a method, performed by an electronic device, of performing an operation based on bending, the method including: sensing bending that deforms a shape of the electronic device; determining a first region, from which the bending is sensed, from among regions of the electronic device; selecting an object indicated by the first region from at least one object displayed on the electronic device; and performing an operation on the selected object.

Abstract: Systems and methods for determining the priority of a set of reports available to a user. The method comprising determining, by a reporting application, a set of reports available to a user, each report including one or more metrics. The method further comprising obtaining metric data associated with each of the set of reports available to the user, accessing one or more priority calculation parameters for each report in the set of reports, calculating a priority value of each report of the set of reports based on the metric data and the priority calculation parameters for each report, and determining one or more high priority reports based on the priority value of each report of the set of reports.

Abstract: Systems and methods provide techniques for more effective and efficient predictive monitoring of a software application framework. For example, various embodiments of the present invention provide methods, apparatuses, systems, computing devices, and/or the like that are configured to enable effective and efficient predictive monitoring of a software application framework using alert signatures for the software application that are generated by using at least one of first-order alert classification machine learning models, temporal-sequential alert classification machine learning models, categorical-sequential alert classification machine learning models, alert classification machine learning models, and second-order alert classification machine learning models.

Abstract: Various embodiments of the present invention provide methods, apparatuses, systems, computing devices, and/or the like that are configured to enable effective and efficient solutions for designing software architecture of collaborative software application frameworks. For example, certain embodiments of the present invention provide methods, apparatuses, systems, computing devices, and/or the like that are configured to enable communications between a collaboration service computing device and a set of m service components via m service plugins comprising internal service plugins and external service plugin, where each service plugin comprises an asynchronous processing queue and a continuous execution thread.