There is provided a system including a memory and a processor configured to obtain a first frame of a video content including an object and a first region based on a segmentation hierarchy of the first frame, insert a synthetic object into the first frame, merge an object segmentation hierarchy of the synthetic object with the segmentation hierarchy of the first frame to create a merged segmentation hierarchy, select a second region based on the merged segmentation hierarchy, provide the first frame including the first region and the second region to a crowd user for creating a corrected frame, receive the corrected frame from the crowd user including a first corrected region including the object and a second corrected region including the synthetic object, determine a quality based on the synthetic object and the second corrected region, and accept the first corrected region based on the quality.

Embodiments of the present disclosure relate generally to semantic indexing to improve search results of a large corpus. Some embodiments, with at least one of the keywords of the search query encoded by a semantic vector in a semantic vector space, identify a plurality of candidate publications in the publication corpus, the plurality of candidate publications encoded by a cluster of a plurality of semantic vectors in the semantic vector space, the identifying based on proximity in the semantic vector space between the at least one of the keywords of the search query and keywords in the plurality of candidate publications, the proximity based on a first machine-learned model that projects the at least one keyword in the search query and the keywords in the plurality of candidate publications into the semantic vector space.

An extensive computer based online math research system (the Research System) having as its foundation an Ontology of mathematics, and utilizing unique and intensive computer support, coordination, data structuring, data storage, computer processing, retrieval capabilities, and data-mining capabilities, and an Ontology editing system that runs on computer software with computer processors and data storage capabilities (the Ontology Editor System). The Research System also includes a methodology to enable online reference and data manipulation of the Ontology, and an Internet based search of the concepts of mathematics and applications of mathematics to the sciences on the basis of the Ontology.

Generating a compliance framework. The compliance framework facilitates an organization's compliance with multiple authority documents by providing efficient methodologies and refinements to existing technologies, such as providing hierarchical fidelity to the original authority document; separating auditable citations from their context (e.g., prepositions and or informational citations); asset focused citations; SNED and Live values, among others.

A non-transitory computer-readable recording medium stores an information processing program for causing a computer to perform a process including: specifying a terminal subject based on a parent-child relationship of subjects that correspond to a plurality of tags used in a document; and calculating a vector of a tag that corresponds to the terminal subject based on each word included in definition information set for the terminal subject and a word vector dictionary that defines a vector of each word.

Systems and methods are disclosed for automated industrial hygiene assessment and display comprising receiving sampling results for a stressor, such as a harmful environmental artifact in a physical environment; deriving one or more codes for the stressor from a digital record via an indexing module and/or from other data sources; generating a health effect rating (HER) based on the code; generating an exposure rating (ER) based on the sampling results; generating an uncertainty rating (UR) based on the sampling results; displaying, an interactive UI to facilitate approval or selection of at least one of the HER, the ER, or the UR; generating at least one of a risk rating (RR) or an information gathering priority rating (IGPR) based on a selection of the at least one of the HER, the ER, or the UR; and displaying via the interactive UI at least one of the RR or the IGPR.

Systems and methods for generating communications based on user account activity data are described herein. For example, the system may obtain an input activity log and generate an activity log transformation. The system may input the activity log transformation into a vector encoding model to obtain a first output vector encoding. The system may input the first output vector encoding into a contrastive machine learning model to obtain a first matching vector encoding. The system may access a first plurality of vector encodings from a communication database. Based on comparing each vector encoding in the first plurality of vector encodings with the first matching vector encoding, the system may generate the matching communication.

In some aspects, the techniques described herein relate to a method including: receiving, at a platform, an electronic document and corresponding metadata; encoding the electronic document; sending the encoded document as a byte stream to a unit extraction service; decoding, by the unit extraction service, the byte stream into a string file; standardizing, by the unit extraction service, partition separation characters; determining, by the unit extraction service and based on the partition separation characters, a value of a first key; assigning, by the platform, a value of the corresponding metadata as a value of a second key; indexing the first key, the value of the first key, the second key, and the value of the second key in a search index; and providing a search function via an interface of the platform, wherein the search function searches the search index.

In some embodiments, the present disclosure provides for an exemplary computer-implemented system that may include a longitudinal data engine, including: a processor and specialized index generation software to generate: an index data structure for a respective event type associated with each respective subject or object; where each respective index data structure is a respective event type-specific data schema, defining how to store events of a particular event type to form longitudinal data of each respective subject or object; an ontology data structure that is configured to describe one or more properties of a respective event of a respective subject or object; and longitudinal data extraction software to extract a respective longitudinal data for a plurality of index data structures and a plurality of ontology data structures associated with a plurality of subjects or objects.

A computer-implemented method comprising: receiving, with a computer, first and second datasets; performing, with the computer, column discovery on the first and second datasets using a first trained machine-learning model to produce a column map that indexes one or more columns in the first dataset to one or more columns in the second dataset; performing, with the computer, row discovery on the first and second datasets using a second trained machine-learning model, a trained approximate nearest neighbor index, and the column discovery to produce a row map that indexes one or more rows in the first dataset to one or more rows in the second dataset; combining, with the computer, the first and second datasets using the column map and the row map to form a combined dataset; and performing one or more actions with the combined dataset.