Abstract: Disclosed herein are systems and method for executing a text-to-speech machine learning model. A method includes: determining a first phoneme embedding from an input phoneme sequence; determining, using a text embedding model, a token-level embedding from an input word sequence, wherein the input phoneme sequence corresponds to the input word sequence; upsampling the token-level embedding into a second phoneme embedding; inputting both the first phoneme embedding and the second phoneme embedding in an encoder-decoder machine learning model configured to generate acoustic features for a vocoder model that produces a speech waveform; and executing the vocoder model to generate speech reciting the input word sequence.

Abstract: Disclosed herein are systems and method for ML-based analysis of racing communications. In one aspect, the method includes: obtaining a plurality of audio message between a plurality of race team members, converting the messages into text format, determining roles of speakers, including at least one of: determining roles of some speakers based on analysis of specific words and/or phrases, and determining roles of other speakers based on analysis of background noise patterns in audio messages, recognizing topics of messages by applying a third neural network trained on racing data, identifying a list of predefined keywordsin the text messages, determining a level of importance of each message based on the role of the speaker, the topic of the message, the predefined keywords, and a relationship of the message with other messages, and displaying the plurality of text messages based on the level of importance in a user interface.

Abstract: Systems and methods for detecting a malware injection interested processes. The method includes identifying one or more trusted processes, monitoring at least one thread associated with the trusted processes using at least one control point, detecting activity at the at least one thread based on the at least one control point and determining a timestamp of the detected activity, receiving from the trusted processes at least one execution stack corresponding to the timestamp and indicating the at least one control point used to monitor the at least one thread, applying a first malware detector to the at least one execution stack to generate a first verdict, collecting the first verdict and auxiliary information corresponding to the trusted processes at the given timestamp, and applying a second malware detector to the first verdict and the auxiliary information to generate a second verdict.

Abstract: A system and method for performing brand detection in a video is disclosed herein. The method comprises receiving the video for performing the brand detection thereon; splitting the video for obtaining a plurality of video frames; performing an open set detection on each input video frame from the plurality of video frames, which comprises proposing one or more bounding boxes on the input video frames on regions of the video frame that potentially include brand media; cropping the one or more bounding boxes; providing the cropped bounding boxes to a classification module for obtaining embedding vectors corresponding to each of the cropped bounding boxes; and comparing the embedding vectors of the cropped bounding boxes with embedding vectors of one or more brand reference images provided by a user for computing instances of brand detection in each video frame of the plurality of video frames.

Abstract: Disclosed herein are systems and method for generating a teaching avatar using machine learning. A method may include training, using a first machine learning algorithm, a teaching avatar to recite information using speech-based mannerisms and physical gestures of a real-life teacher, wherein the training is performed with a training dataset comprising videos and transcripts of real-life teachers administering courses. The method may include receiving a class attribute comprising information about at least one student of a course. The method may include setting a visual appearance and an audio configuration of the teaching avatar based on the class attribute. The method may including generating, using a second machine learning algorithm, a script based on the course. The method may include executing, on a computing device, the teaching avatar to recite the script with the speech-based mannerisms and physical gestures of the real-life teacher.

Abstract: Disclosed herein are systems and method for______.

Abstract: Disclosed herein are systems and methods for generating realistic handwriting movements for a virtual avatar. An exemplary method includes: receiving an input comprising one of a drawing or text; assigning a coordinate and a timestamp to each respective point on the input; generating a curve including a plurality of coordinates assigned to points in the input; generating a weighted virtual object configured to trace the curve in an animation based on an order of a plurality of timestamps assigned to the points in the input, wherein the weighted virtual object has an inertial mass parameter that modifies the curve to represent different writing variations; configuring a hand of a virtual avatar to move along a modified version of the curve as traced by the weighted virtual object with the inertial mass parameter being set to a first value; and generating, for display, the avatar as hand writing the input.

Abstract: Disclosed herein are systems and method for training neural networks to identify and geolocate racers, comprising: obtaining a first dataset, a second dataset, and a map of the race course with unique geolocations; generating a first training dataset comprising the first dataset and geolocation labels identifying the unique geolocations; training a geolocation identification neural network to identify at least one unique geolocation in the images of the race course and to identify corresponding unique geolocations on the map of the race course; generating a second training dataset comprising the second dataset and racer labels identifying each racer in the images of racers; training a racer identification neural network to identify at least one racer in the images of racers based on identifying visual appearances of each racer; and using the trained geolocation identification neural network and the trained racer identification neural network to analyze racing videos to identify and geolocate positions of race

Abstract: Disclosed herein are systems and method for synchronizing race telemetry, videos, and map data. In one aspect, a method includes: obtaining racing videos of racers on a race course and a map of the race course; identifying unique geolocations of the race course; executing a trained racer identification neural network to visually identify and track at least one racer at least at the identified unique geolocations in the racing videos; obtaining telemetry data associated with absolute race time for each identified racer; synchronizing the racing videos, the map, and the telemetry data for each identified racer based on the identified unique geolocations and the absolute race time; and generating a dynamic user interface (UI) for displaying time-synchronized videos comprising a visual identifier of each racer, the map including a visual identifier of the geolocation of each racer on the race course, and the telemetry data for each racer.

Abstract: A system generates a first dataset with input images of vehicles and corresponding output vectors identifying the vehicles. The system also creates a second dataset with images of damaged vehicles and output vectors detailing the damages. The system trains a machine learning model using the first dataset to detect vehicles in images, employing backbone and linear layers. The system then fine-tunes the model with the second dataset to identify damages on detected vehicles, updating the weights of the backbone layers during initial training and the first linear layer during fine-tuning. The system processes an input image of a vehicle through the trained model to detect and display any damages on a user interface, highlighting the vehicle and its damages.

Abstract: Systems and methods for analyzing images using deep learning and computer vision models. Automatic analysis of photographic images allows, for example, for the identification of important elements in these images, such as detection and measurement of vehicle details of interest to racing teams. Racing vehicles, typically cars that use standardized components, have specific geometry that can be detected and used for specific detection tasks.

Abstract: Disclosed herein are systems and methods for generating realistic movements for a virtual avatar. An exemplary method includes: extracting, using a speech recognition algorithm, a plurality of words from an audio clip; inputting the plurality of words into a machine learning model configured to output a plurality of gestures to accompany the plurality of words, wherein the machine learning model is configured to: detect a group of words; identify a keyword in the group of words; and assign, to the group of words, a gesture corresponding to the keyword; and animating a virtual avatar to perform the outputted plurality of gestures while reciting the plurality of words, wherein the gesture is performed when reciting the group of words.

Abstract: Systems and methods for automatically enhancing the quality of images in the training set of a neural network NN. A method includes gaining access to a training set including a plurality of images. Using at least one image quality assessment method, at least one image is identified from a plurality of images in the training set, which matches a low-quality criterion as at least one low-quality image. At least one image enhancement method is used for enhancing the at least one low-quality image to obtain at least one enhanced image. The at least one low-quality image is replaced with the corresponding at least one enhanced image in the training set.

Abstract: A system and a method for performing brand detection and brand analysis in a video are disclosed herein. The method comprises receiving the video for performing the brand detection thereon; splitting the video for obtaining input video frames; performing an open set detection on the input video frames to compute instances of detecting brand media; determining an exact square region in which the brand media is occupied within the input video frame; resolving a scene understanding task in the input video frame; detecting crucial moments in the video; identifying an area on the input frame where a user's attention is focused to provide user focus index; generating heat maps using the detection of crucial moments and user focus index; and combining above inputs from the brand detection and the scene understanding into the heat maps for all the input video frames of the video for computing a brand advertising rate.

Abstract: Disclosed herein are systems and method for integrating content into a sequence using machine learning. A method may include: receiving, via a user interface (UI), content describing a topic and a plurality of sub-topics associated with the topic; executing a first machine learning model configured to determine compatibility scores between the content and a plurality of curricula; identifying at least one curriculum with a compatibility score greater than a threshold compatibility score; executing at least one other machine learning model configured to generate a modified curriculum in which the content is inserted into an original sequence of courses associated with the at least one curriculum based on prerequisites of the content and available resources to provide access to the content; and outputting, on the UI, the modified curriculum.

Abstract: Disclosed herein are systems and method for generating synthesized content using machine learning. A method may include: receiving, via a UI, a first user selection of a topic from a plurality of topics; identifying a first reference material and a second reference material from a plurality of reference materials related to the topic; determining a first complexity level and a first quality level of the first reference material; determining a second complexity level and a second quality level of the second reference material; calculating a weight distribution that is a combination of a ratio between the complexity levels and a ratio between the quality levels; executing a machine learning algorithm that generates content synthesized from both the first reference material and the second reference material based on the weight distribution; and outputting, for display, the content on the UI.

Abstract: Disclosed herein are systems and method for updating courses generated using machine learning. A method may include: receiving, via a user interface (UI), a user selection of a preferred duration for consuming course content associated with a topic; generating, using a machine learning algorithm at a first time, a course from reference materials describing a plurality of sub-topics associated with the topic, wherein the machine learning algorithm combines the reference materials in an organizational scheme such that a length of the course is not greater than the preferred duration; outputting the course on the GUI; detecting, at a second time, a new reference material describing a new sub-topic for inclusion in the course; modifying, using the machine learning algorithm, the course to include the new sub-topic in a manner such that the length of the course is not greater than the preferred duration; and outputting the modified course.

Abstract: disclosed herein are systems and method for generating custom courses using machine learning. a method may include: receiving, via a user interface (UI), a first user selection of a topic; retrieving content associated with the topic from a database of reference materials; generating, for display on the GUI, the content in a default organizational scheme; receiving, via the GUI, a second user selection to organize the content in a custom organizational scheme of a preferred duration for consuming the topic; determining, by a hardware processor, an amount of time needed by a user to consume the content in the default organization scheme; and automatically updating the content displayed in the UI in accordance with the custom organizational scheme based on the preferred duration and the amount of time.

Abstract: Disclosed herein are systems and methods for updating a graphical user interface displaying content related to a topic based on user performance. A method may include: receiving, via a user interface (UI), a user selection of a topic; generating, using a machine learning algorithm, a course from reference materials describing a plurality of sub-topics associated with the topic, wherein the machine learning algorithm includes, in the course, a plurality of assessments that test comprehension of the plurality of sub-topics; outputting the course on the UI; monitoring user interaction with a first subset of the assessments within the course on the UI; in response to determining, based on the monitoring, that the user interaction does not meet a comprehension criteria, modifying, using the machine learning algorithm, a first subset of the sub-topics corresponding to the first subset of the assessments; and outputting the modified course on the UI.

Abstract: A system determines whether to execute a first operation of a distributed machine learning model (MLM) on at least one server or on at least one client device. In response to determining that the first operation should be executed on the at least one server, the system: encrypts data associated with the first operation using a specific encryption scheme; and transmits the encrypted data to the at least one server for execution of the first operation on the encrypted data. In response to determining that the first operation should be executed on the at least one client device, the system performs the first operation on the data using the at least one client device without encrypting using the specific encryption scheme.