Abstract: A system for storing input data to parameters included in a view using a generic save data API is provided. The system includes at least one processor configured to execute instructions to: make a save data API call for saving the input data to a plurality of tables, wherein an input of the save data API call comprises identifiers of the plurality of tables and the parameters, and the input data; obtain information of a second application from resource information pre-registered in the first application, the resource information mapping applications to tables respectively associated with the applications; send, based on the obtained information of the second application, a request to the second application to store first input data, from among the input data, to at least one table associated with the second application; and store, by the second application, the first input data to the at least one table.

Abstract: Systems and methods are described for viewing missed media content. A media content stream is received at a user device. A first timestamp is logged, in response to determining a first failure in the receiving of the content stream, indicating when the first failure in the receiving of the content stream to the user device occurred. A second timestamp is logged, in response to determining a first restoration of the receiving of the content stream, indicating when the first restoration in the receiving of the content stream to the user device occurred. A first recorded portion of the media content stream is retrieved. The first recorded portion has a start point corresponding to the first timestamp and an end point corresponding to the second timestamp.

Abstract: A vehicle includes a controller programed to: collect a set of data related to a driver of the vehicle; predict a driving setting for the driver using the set of data and an initial student-T process (STP) machine learning (ML) model; generate an updated STP ML model based on the prediction of the driving setting as to the set of vehicle data; transmit incremental learning related to the updated STP ML model to a server; and receive, from the server, a personalized driving setting for the driver output from a cloud STP ML model trained by the incremental learning.

Abstract: A system for saving history information of input data to parameters included in a view is provided. The system includes a processor configured to: output an instance of the view; obtain pre-stored view details for the view, including identifiers of the parameters and tables to which input data of the parameters included are to be stored; make a generic API call to save current input data of a parameter to a table, wherein an input to the API call includes the current input data and old data of the parameter; based on the current input data and the old data being different, store the current input data in the table, create history information of the current input data and store the history information in a history table.

Abstract: Systems and methods for embodiments of artificial intelligence systems for identity management are disclosed. Embodiments of the identity management systems disclosed herein may support the correlation of identities determined authoritative source systems with uncorrelated accounts within an enterprise using artificial intelligence techniques.

Abstract: A method, performed by at least one processor, for adding user-defined custom fields to a user interface (UI) view of a previously-developed application, includes: outputting a first screen for creating or editing a UI view of the previously-developed application; receiving a user selection to add a new user input field to the UI view; and storing one or more parameters of the new user input field in association with a reserved field of a reserved database of the previously-developed application, such that values input to the new user input field during execution of the user interface view are saved to the reserved field. The previously-developed application includes a plurality of predefined user input fields predefined during application development, a corresponding plurality of predefined database fields mapped thereto, and the reserved database comprising a predetermined number of reserved fields for mapping to new user input fields added post-development.

Abstract: A system for bulk updating resource data of parameters included in a view is provided. The system includes at least one processor configured to: receive a user request to bulk import data in a file including rows of the data, each row corresponding to a different instance of the view; make an import data API call to bulk import the data into one or more tables to which the data of the parameters is to be saved; make a save data API call to update existing resource data of the parameters, stored in the one or more tables, with the data included in the file; and update, in the one or more tables, the existing resource data of the parameters for the plurality of instances with the data included in the file, wherein the import data save data APIs are generic across a plurality of views and tables.

Abstract: Techniques for detecting erroneous LIDAR data are disclosed herein. One embodiment receives LIDAR point-cloud data pertaining to a robot's environment; receives image data and generates segmented optical-flow data therefrom; fuses, in a 2D grid, a plurality of objects including LIDAR points and optical-flow pixels; executes a hash function that generates, for the plurality of objects, a 1D hash table and an associated index; performs one or more queries using the 1D hash table and the associated index to measure the extent of spatial correspondence between the LIDAR points and the optical-flow pixels; identifies the LIDAR point-cloud data as erroneous, when the extent of spatial correspondence fails to satisfy one or more predetermined criteria; and identifies the LIDAR point-cloud data as valid and controls operation of the robot based, at least in part, on the LIDAR point-cloud data, when the extent of spatial correspondence satisfies the one or more predetermined criteria.

Abstract: A speed limiter for a vehicle can be personalized. The speed limiter can be caused to be activated. A user can provide a user input on a user interface. The user input can include a speed limiter personalization setting. In response to the user input, the speed limiter personalization setting can be caused to be implemented.

Abstract: A system for conditional validation in forms includes at least one processor, and at least one memory coupled to the at least one processor and configured to store executable instructions. The executable instructions, when executed by the at least one processor, cause the at least one processor to visually present a plurality of parameters for defining at least one condition for controlling a property of a first field of a form in a configuration mode, generate a condition corresponding to a set of user inputs for the plurality of parameters, and store the generated condition with the form. The condition is based on a second field different from the first field.

Abstract: A system for conditional validation in forms includes at least one processor, and at least one memory coupled to the at least one processor and configured to store executable instructions. The executable instructions, when executed by the at least one processor, cause the at least one processor to visually present an expression input area for accepting user input of at least one expression defining at least one condition for controlling a property of a first field of a form in a configuration mode, generate the at least one condition corresponding to the at least one expression, and store the generated at least one condition with the form. The at least one expression comprises at least one of a logic expression or a mathematic expression.

Abstract: Systems and methods are provided for programmatically determining a remote operator of an autonomous or semi-autonomous vehicle. For example, some implementations may relate to assigning and reassigning a remote operator user in accordance with real-time changes in environmental characteristics of the distributed network of vehicles, remote operators, and (potentially) drivers.

Abstract: A method for time sensitive network (TSN) operation includes: in the case of data traffic stoppage in a communication channel of TSN due to at least one of down time of a user plane (UP) connection and a reset of the UP connection, performing the following: generating, by a user plane function (UPF), an audit report about the at least one of the down time and the reset; sending, by the UPF, the audit report to a first element in control plane (CP), without waiting for communication from the CP; and determining, by one of the first element or a second element in the CP, survival time of the communication channel. The audit report is sent along with a report type indicating the down time of the UP connection due to connection fluctuation or a switch-over of the UP connection from a primary UPF to a back-up UPF.

Abstract: Systems and methods for controlling a cruise control system of a vehicle using the moods of one or more occupants are described herein. In one example, a system includes a processor and a memory in communication with the processor. The memory includes instructions that, when executed by the processor, cause the processor to determine, using a mood model, the mood of at least one occupant of a vehicle and a setting for a cruise control to maintain a distance between the vehicle and a preceding vehicle based on the determined mood. The instructions then cause the processor to set the cruise control to operate according to the setting.

Abstract: An active road surface maintenance system and method developed for connected vehicles with the aid of a mobility digital twin (MDT) framework. A method performed in a cloud-based digital space includes receiving data regarding a physical object from a physical space connected to a vehicle. The method also includes processing the data using machine learning to model road surface conditions, in which respective penalty values are assigned to corresponding road surfaces, a respective penalty value being higher the lower a condition of the corresponding road surface. The method also includes deriving instructions based on the modeled road surface conditions and the respective penalty values to guide actuation of the vehicle along a trajectory. The method further includes transmitting the instructions to the physical space connected to the vehicle to guide actuation of the vehicle.

Abstract: Responsive to a user instruction or a security breach occurring in an enterprise computing environment, an emergency shutdown and restore module is adapted to obtain and evaluate an identity population definition to determine a population of identities (e.g., a forensic team) associated with accounts distributed across applications in the enterprise computing environment. The emergency shutdown and restore module is further adapted to determine source systems of such accounts and communicate with those source systems via source-specific connectors. The emergency shutdown and restore module can respectively request the source systems to shut down access to the applications by the accounts associated with the population of identities, or to exclude the accounts associated with the population of identities in shutting down access to the applications. After performing a security breach analysis, the emergency shutdown and restore module can request the source systems to restore access respectively.

Abstract: An approach is provided for stop classification and journey extraction from vehicle location trace data. The approach involves, for example, processing vehicle location trace data to determine a sequence of vehicle stop locations. The sequence of vehicle stop locations comprises a first stop location, a second stop location, and a third stop location in chronological order. The approach also involves determining a first route cost (e.g., a first route length) from the first stop location to the third stop location via the second stop location and a second route cost (e.g., a second route length) from the first stop location directly to the third stop location. The approach further involves determining a classification of the second stop location as either a task stop or a rest stop based, at least in part, on a comparison of the first route length and the second route length.

Abstract: A method may include receiving training data comprising a time series of gaps between an ego vehicle and one or more lead vehicles at a plurality of time steps, embedding the training data into a fixed-length sequence, inputting the fixed-length sequence into a Transformer-RNN model comprising a Transformer component and an RNN component, wherein the transformer component applies attention to each data point of the fixed-length sequence based on a fixed number of previous inputs, and training the Transformer-RNN model, using the training data, to output a predicted gap at a future time step based on an input sequence of gaps.

Abstract: A method may include learning reward functions for a plurality of first vehicles based on first vehicle data associated with the plurality of first vehicles using inverse reinforcement learning, associating each vehicle of the plurality of first vehicles with one cluster among a plurality of clusters based on the reward functions, determining a centroid reward function for each of the clusters based on the reward functions associated with each cluster, performing a comparison between second vehicle data associated with a second vehicle and the first vehicle data, determining a vehicle among the plurality of first vehicles having associated first vehicle data that is most similar to the second vehicle data based on the comparison, associating the second vehicle with the cluster associated with the determined vehicle, and controlling operation of the second vehicle based on the centroid reward function of the cluster associated with the second vehicle.

Abstract: Systems and methods for cloud security monitoring and threat intelligence in accordance with embodiments of the invention are disclosed. In one embodiment, a process for monitoring and remediation of security threats includes generating a threat model using a first portion of activity data, identifying, based upon the threat model, a threat using a second portion of activity data, selecting a security policy to implement in response to the identified threat, identifying cloud security controls in a remotely hosted cloud application server system to modify in accordance with the selected security policy, establishing a secure connection to the remotely hosted cloud application server system using login credentials associated with a tenant account with the cloud application, and sending instructions to the remotely hosted cloud application server system to set the identified cloud security controls with respect to the tenant account in accordance with the selected security policy.