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.

Abstract: A method and system is disclosed for generating video content where speech audio is accurately synchronized with facial movements. The method begins by converting input text into speech audio using a text-to-speech (TTS) system. An original video is then processed to identify and extract the mouth area of the human face. The method generates a plurality of frames of the mouth area corresponding to segments of the speech audio using a trained generative neural network. These frames are lip-synced with the corresponding audio segments. The extracted mouth area frames are overlaid onto the original video, followed by performing facial reconstruction using a trained facial reconstruction model to correct distortions and achieve a natural and expressive appearance around the mouth area. Finally, the generated speech audio is combined with the modified video to produce a final video with synchronized lip movements and phonetic sounds.

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: Aspects of the present disclosure include a method for verifying live user presence in an online session, comprising obtaining at least one video stream of a user captured during at least one previous presentation of at least one user interface (UI) on a display, obtaining input events representing previous user interactions with the UI, generating a behavioral digital fingerprint profile based on the video stream and the input events, initiating an online exam during the online session by providing, for presentation on the display, at least one additional UI, receiving at least one additional video stream of the user during the exam, receiving additional input events representing user interactions with the additional UI, determining exam-specific behavioral features based on the additional video stream and the additional input events, and verifying whether the user is taking the exam based on at least one of the features or the profile.

Abstract: Virtual file honeypots (VFHs) are deployed to protect real files in a computing system. The storage footprint is reduced because the virtual file honeypots do not require storage resources. VFHs are generated to appear the same as other files in a computing system, presenting an attractive attack surface as bait to reveal malicious processes and agents. The VFHs are generated on-demand and are not backed up in system storage. The use of VFHs requires less storage capacity than real honeypot files and conserves computing system resources.

Abstract: A system generates, for display on a computing device, an avatar based on an avatar model specifying visual characteristics and movement capabilities of the avatar. The system receives a request to reduce computational costs for generating the avatar by a specified amount. The system executes a machine learning model trained to select one or more of the visual characteristics and the movement capabilities of the avatar to adjust based on available computational resources and computational costs to utilize the available computational resources. The system adjusts the one or more of the visual characteristics and the movement capabilities of the avatar outputted by the machine learning model in response to the request.

Abstract: Disclosed herein are systems and methods for automated grading of written response tests. The method includes obtaining course material, test materials for a plurality of courses offered by an academic institution, a standardized scoring rubric for a plurality of tests and different courses, and grading guidelines comprising test scoring rules for the written response test or course. The method further includes analyzing the course materials and test materials using a trained rubric customizer LLM. The method also includes obtaining a written response from a learner for a test in a course taken by the learner. The method further includes analyzing the written response using two or more trained test grading LLM agents executing in parallel with each other. The method further includes combining, by a scoring engine, a plurality of criteria scores from the one or more trained test grading LLM agents. The method further includes generating a grading report.

Abstract: Systems and methods provide a multidimensional data structure to tailor data storage and corresponding user output. Tailored graph data structures reduce the level of hallucination in composing an answer to a user using a large language model. In response to a user query of documents, the user query is translated to a vector. From the vector, the most relevant pieces of documents that are as similar as possible in semantics to the question of a person can be determined and communicated to the user. In one aspect, using the vector base (vector query applied to the documents), a graph can be generated in the form of nodes and connections between data in the documents.

Abstract: Disclosed herein are systems and method for a dynamic knowledge graph (KG) augmentation of an LLM. The method includes obtaining a LLM chat history between a user and the LLM enhanced with a KG database. The LLM chat history includes user queries, relevant knowledge data retrieved from the KG database, and LLM answers for each user query. The LLM answers are based at least in part on relevant knowledge data retrieved from the KG database. The method also includes applying a KG update MLM agent trained to: analyze the LLM chat history, KG database and the graph schema and to identify: (i) a missing knowledge data from the KG database, and/or (ii) a missing relationship between knowledge data nodes in the KG database. The method further includes after identifying missing knowledge data and/or the missing relationship, updating the KG database with new knowledge data and/or new relationship between nodes.

Abstract: Disclosed herein are systems and methods for providing a machine learning (ML)—assisted presentation hosting platform. An example method of preparing ML models of the presentation hosting platform comprises: receiving a first training dataset comprising: a plurality of different presentations having a plurality of different activities, one or more presentation scenarios for each presentation, and one or more devices and/or software that perform the plurality of activities; training a first ML model using the first training dataset to predict presentation scenarios for different types of presentations; receiving a second training dataset comprising a mapping between the one or more presentation scenarios and a plurality of devices and/or software with different configuration, resources, capabilities, and/or load; and training the second ML model using the second training dataset to predict a mapping of the activities of each presentation to devices and/or software of participants of the presentation.

Abstract: Aspects of the present disclosure include a method for verifying live user presence in an online session. The method comprises providing, for presentation, a time-varying pattern and an instruction to a user to perform one or more requested user actions in synchrony with the pattern. The method further comprises receiving one or more data streams of the user during the presentation, extracting from the data streams feature information indicative of one or more detected user actions in the data streams, and generating one or more event signals based on the feature information. The method further comprises determining, based on the event signals and the pattern, a first measurement and a second measurement indicative of spatial correspondence and temporal alignment, respectively, between the detected user actions and the pattern, and verifying whether the user is a live person based on the first and second measurements.

Abstract: A system obtains, using a camera, a video stream of a user viewing a display. In response to changing visual characteristics of an object displayed on the display, the system detects visual changes on at least one surface of the user in the video stream. The system, based on a determination that the visual changes are within one or more thresholds, determines that the user is in front of the display. The system, based on a determination that the visual changes are not within the one or more thresholds, transmits a message that the user is not in front of the display.

Abstract: Disclosed herein are systems and methods for generating a prompt with context based on a list of topics generated for responses from large language models (LLMs). In one aspect, the method includes: obtaining a query from a user; generating and transmitting a prompt based on the query for input into a first and second LLMs; obtaining a first selected portion of the first response from the first LLM and at least a second selected portion of the second response from the second LLM; generating a list of topics using a trained topic analysis machine learning model (MLM) to identify topics from the responses; and generating a prompt for input into the third LLM utilizing at least one topic from the list of topics using at least the selected portions from the first LLM or the selected portions from the second LLM.

Abstract: A system executes a first machine learning model to identify camera parameters of an input image. The system generates sets of shifted camera parameters by applying a set of predefined delta values to each of the camera parameters and calculates a plurality of homography matrices. The system executes a second machine learning model to identify a plurality of keypoints in the input image. For each respective set of the shifted camera parameters: the system projects the plurality of keypoints to planar coordinates using a homography matrix from the plurality of homography matrices that corresponds to the respective set of the shifted camera parameters, calculates a line of best fit that comprises the planar coordinates; and determines a total difference between the line of best fit and each of the planar coordinates. The system identifies a set of the shifted camera parameters with a minimal value of difference.

Abstract: A system receives an avatar model and target specifications for executing the avatar model on a computing device, wherein the avatar model comprises visual characteristics and movement capabilities of an avatar. The system generates, for display on the computing device, the avatar based on the avatar model. The system monitors specifications of the computing device. The system detects a difference between the target specifications and the specifications monitored. The system adjusts, based on the difference, one or more of the visual characteristics and the movement capabilities of the avatar while the avatar is displayed on the computing device.

Abstract: A system receives scanned surface data of a physical race track. The system identifies imperfections that are not included in a virtual model of the physical race track based on the scanned surface data. The system receives sensor data capturing a first plurality of parameters of a vehicle moving along a driving path on the physical race track. The system maps, for a training dataset, at least one identified imperfection to at least one parameter in the first plurality of parameters in response to determining that the at least one parameter was captured at a location on the driving path that matches a location of the at least one identified imperfection on the physical race track. The system trains and executes a path imperfections ML model to detect track imperfections based on an input set of parameters and an input driving path using the training dataset.