Abstract: A system receives a user request to mount a child frontend application comprised in a primary frontend application. The system, in response to receiving the user request, checks whether the child frontend application is in a cache. The system, in response to determining that the child frontend application is not in the cache, mounts the child frontend application and initializes the child frontend application using a first lifecycle function that performs initial setup for the child frontend application. The system stores the child frontend application in the cache using a second lifecycle function for saving a current state of the child frontend application.

Abstract: Systems and methods of persistent file protection including determining an incident, intercepting a file operation in a kernel mode based on the incident, determining file information associated with a file to be modified by the file operation, storing the file information in a persistent file cache (PFC), tracking a file change between system shutdown and system restart, receiving a remediation action based on the incident, and performing remediation of the file using the file information stored in PFC.

Abstract: Disclosed herein are systems and methods for training an embedding transformation model to predict a main embedding. The method includes receiving an input query to a LLM service. The method also includes determining if a main embedding of the query is provided by a main embedding model. The method also includes generating a secondary embedding of the query by a pre-trained fallback embedding model. The method further includes based on a determination that the main embedding of the input query is provided by the main embedding model, preparing a embedding transformation model using the main and secondary embeddings to predict the main embedding based on the secondary embedding.

Abstract: Computer systems and methods with decentralized blockchain orchestration architecture use multiple orchestrators embedded at the cluster level. A push architecture is provided for manipulating and verifying workload configurations, including the ability to determine differences between stored configurations and actual configurations.

Abstract: Disclosed herein are systems and methods for generating LLM embeddings. The method includes receiving an input query to a LLM service. The method also includes receiving an input query to a LLM service; determining if a main embedding of the query is provided by a main embedding model; based on a determination that the main embedding of the input query is not provided by the main embedding model, generating a secondary embedding of the query by a pre-trained fallback embedding model, and applying an embedding transformation model to the secondary embedding to predict the main embedding and transmitting the predicted main embedding to the LLM service.

Abstract: Methods and systems for enhancing a student's comprehension of visually narrated lectures by automatically augmenting narration of textual lectures with automatically generated textual scenarios inserted into the lecture, including by automatically selecting the locations of the insertion, contents, voice, and avatar characteristics for the scenarios.

Abstract: A system may store user data associated with a usage of a software application. A system may receive a request for the user data from a developer of the software application. A system may determine whether the user data is generated from a first version of the software application or a second version of the software application. A system may in response to determining that the user data is generated from the first version of the software application, retrieving a first dictionary that indicates an anonymization scheme for transmitting the user data of the first version of the software application, wherein each version of the software application has a different dictionary. A system may execute the anonymization scheme on the user data to generate anonymized user data. A system may transmit the anonymized user data to the developer in response to the request.

Abstract: Systems and methods of persistent file protection integrate cloud-based backups with EDR file protection. Use of local persistent file cache (PFC) data with cloud-stored file segments, facilitates reconstruction of file states from multiple sources. File storage and retrieval efficiency are optimized, even when local session caches are invalidated or when extensive file modifications exceed storage quotas.

Abstract: A system monitors a vehicle navigating in an environment using a pre-trained machine learning model that is configured to make navigation decisions during autonomous driving of the vehicle. The system identifies a first simulated trajectory generated by the pre-trained machine learning model and a first real trajectory driven by the vehicle. In response to determining that a trajectory type of the first real trajectory is one of a plurality of trajectory types that trigger fine-tuning of the pre-trained machine learning model, the system calculates a discrepancy value between the first real trajectory and the first simulated trajectory. In response to determining that the discrepancy value is greater than a threshold discrepancy value, the system updates weights of the pre-trained machine learning model and executes the pre-trained machine learning model with the updated weights.

Abstract: Disclosed herein are systems and methods for providing LLM embeddings. The method also includes receiving a query to a LLM service. The method also includes determining if a first embedding of the input query is provided by a main embedding model. The method further includes, based on a determination that the first embedding is provided by the main embedding model, identifying an embeddings cluster similar to the input query, and computing a main centroid embedding for the embeddings cluster based on the first embedding. The method further includes based on a determination that the first embedding is not provided by the main embedding model, identifying an embeddings cluster similar to the input query, and transmitting the main centroid embedding of the embeddings cluster to the LLM service.

Abstract: Systems and methods include detecting obstacles and drivable areas by an autonomous vehicle by inputting image and map data into a neural network to extract feature vectors. A transformer encoder converts these vectors from camera space to Bird's Eye View (BEV) space. A detection head identifies objects, and a segmentation head generates a BEV map showing objects and drivable surfaces. Attributes from both heads are compared, and the segmentation head's weights are updated accordingly, resulting in an updated BEV segmentation map output by the updated segmentation head.

Abstract: Disclosed herein are systems and method for repurposing a machine learning model. An exemplary method includes: receiving a first training dataset; determining an input portion and an output portion in an entry of the first training dataset; comparing the first training dataset to a second training dataset used to train a machine learning model, wherein the comparing includes determining a similarity score between the input portion and the output portion of the first training dataset and an input portion and an output portion of the second training dataset; in response to determining that the similarity score is greater than a threshold similarity score, re-training the machine learning model using the first training dataset; and executing the re-trained machine learning model on an input value to generate an output value corresponding to the first training dataset.

Abstract: Disclosed herein are systems and methods for providing LLM embeddings. The method also includes receiving a query to a LLM service. The method also includes determining if a first embedding of the input query is provided by a main embedding model. The method further includes, based on a determination that the first embedding is provided by the main embedding model, identifying an embeddings cluster similar to the input query, and computing a main centroid embedding for the embeddings cluster based on the first embedding. The method further includes based on a determination that the first embedding is not provided by the main embedding model, identifying an embeddings cluster similar to the input query, and transmitting the main centroid embedding of the embeddings cluster to the LLM service.

Abstract: A system obtains a weight matrix associated with a linear operation, the weight matrix comprising a first non-zero value and a second non-zero value that is a negative value. The system decomposes the weight matrix into a first binary matrix indicating positions of the first non-zero value and a second binary matrix indicating positions of the second non-zero value. The system encrypts vector elements of an input vector using an encryption scheme to obtain an encrypted input vector. For each row of the first and second binary matrices, the system homomorphically processes selected encrypted elements of the encrypted input vector to generate at least two intermediate encrypted aggregates corresponding to respective contribution signs. The system generates an output element for each row based on a combination of the intermediate encrypted aggregates.

Abstract: Disclosed herein are systems and methods for proctoring online examinations by determining a gaze of a user. The method includes: performing a calibration process to calibrate a field of view of a user taking an online examination on a computer; after the calibration process is completed, obtaining a gaze origin and a gaze vector of the user using a prepared neural network to obtain facial key points of the user and determining, by a webcam or camera, the gaze of the user in three-dimensional (3D) coordinates to determine whether the gaze vector of the user is within a defined boundary relative to the gaze origin; and based on a determination that the gaze vector of the user is outside the defined boundary for a period of time, transmitting a message that the gaze of the user is outside of the defined boundary.

Abstract: A system receives, via a user interface (UI), an input specification indicating a topic and properties for generating a custom course comprising a plurality of content blocks. For each respective content block of the plurality of content blocks, the system generates, using a machine learning, the respective content block that describes a portion of the topic; prior to generating a subsequent content block of the plurality of content blocks, the system analyzes a set of properties associated with the respective content block to assess whether the respective content block includes information beyond a scope of the topic as indicated in the input specification, in response to determining that the respective content block includes the information, the system modifies the respective content block; and in response to determining that the respective content block after modification does not include the information, the system generates the subsequent content block.

Abstract: Systems and methods for web UI development, including supporting and rendering UI using multiple frameworks and migrating between different frameworks. Systems and methods can train different adapters for converting UI models in declarative abstract view into framework-specific code and render UI from the code using multiple or optimal frameworks.

Abstract: Application integration within a cloud platform system based on declarative callback configuration including registering an application at a vendor portal module to acquire a unique vendor and application identification, declaring callbacks associated with application events, each with a callback type identifier and corresponding data structure, and initiating an API callback by a client module in response to an event, and forwarding the callback to an API Callback Gateway, which verifies the API callback for compliance, encapsulates it with authentication data, and transmits it to a designated callback handler within an ISV infrastructure, processing the callback by the handler, sending a structured response back to the Gateway, which validates and provides the response to a client module, and invoking the cloud platform API using the registered application identifier to trigger the API callback.

Abstract: A system determines whether a first operation performed by an MLM is compatible with one of a first encryption scheme and a second encryption scheme, wherein the MLM is distributed over at least one client device and at least one server. In response to determining that the first operation is compatible with the first encryption scheme, the system: encrypts data associated with the first operation using the first encryption scheme; and transmits the data encrypted by the first encryption scheme to the at least one server configured to apply the first operation. In response to determining that the first operation is incompatible with the first encryption scheme, the system: encrypts the data associated with the first operation using the second encryption scheme; and transmits the data encrypted by the second encryption scheme to the at least one server configured to apply the first operation.

Abstract: Aspects of the present disclosure include a method for verifying live user presence in an online session, comprising receiving first and second video streams capturing a user during the session from a first position and a different second position, respectively. The method further comprises initiating a challenge by providing, for presentation on a different second display of a second device, a code, and providing, for presentation on a first display, an instruction to the user to respond to the challenge using the second device. The method further comprises detecting, based on at least one of the video streams, physical actions of the user during the challenge, receiving, from the second device, user input events representing a user response to the challenge, and determining, based on at least one of the physical actions or the user input events, whether the user successfully completed the challenge.