Abstract: A method for aligning a text with a media material, an apparatus, and a storage medium are provided. The method includes: determining a set of anchor points in the text; performing following operations i) to v) repeatedly until all anchor points are removed from the set of anchor points or all media materials are removed from a set of media materials: i) ranking the anchor points in the set of anchor points, ii) selecting a target anchor point from the set of anchor points based on the ranked anchor points in the set, iii) determining, from the set of media materials, a media material matching a text segment starting from the target anchor point, iv) removing the target anchor point, and v) removing the media material matching the text segment starting from the target anchor point; and aligning the text segments with respective media matching materials.

Abstract: Technology is disclosed herein for the integration of spreadsheet environments with LLM services. In an implementation, an application receives a natural language input from a user associated with a spreadsheet hosted by the application. The application generates a prompt that includes metadata about the spreadsheet and identifies a required format for descriptions of pivot tables. The application sends a prompt to a large language model (LLM) service to elicit a reply that includes a description of the pivot table having the required format. The application receives a reply to the prompt from the LLM service that includes the description of the pivot table in the required format. The application generates pivot table according to the reply from the LLM service.

Abstract: A method of generating a unified graph is described. First graph data is received from a first data source, nodes within the first graph data including first, second, and third fields. Second graph data is received from a second data source, nodes within the second graph data including first, second, and fourth fields. The unified graph is generated from the first and second graph data using aggregated fields of the first field and the third field as a unique identifier of a unified schema for nodes within the unified graph having a first type and aggregated fields of the first field and the fourth field as a unique identifier of the unified schema for nodes within the unified graph having a second type, wherein nodes of the unified graph represent entities and edges of the unified graph represent relationships among the entities.

Abstract: The system receives a request associated with a database including a query and a type associated with the database. The request is expressed in a language not native to the database. The query includes a command in the language not native to the database. The type associated with the database can include an in-memory database, a NoSQL database, or a relational database. Based on the type associated with the database, the system can create a corresponding command native to the database by translating the query to the corresponding command native to the database. The system can connect to the database, execute the corresponding command native to the database, and receive an output of the corresponding command native to the database. The system can translate the output of the corresponding command native to the database into the language not native to the database and provide the translated output to the user.

Abstract: Disclosed is a system, method, and computer program product that generates masked data from within a database infrastructure. Instead of requiring an external tool to generate masked data, a database generates masked data using its internal processing mechanisms.

Abstract: The method involves configuring a pretrained text to music AI model that includes a neural network implementing a diffusion model. The process includes receiving audio sample data corresponding to a specific audio concept, generating a concept identifier token based on the audio sample data, adapting a loss function of the diffusion model based on the concept identifier token, selecting pivotal parameters in weight matrices in a self-attention layer of the neural network of the AI model based on the audio sample data, and further training the pivotal parameters of the AI model, to optimize the AI model for the specific audio concept.

Abstract: A method includes receiving, by a data processing apparatus and from a content distribution system, a message comprising a probabilistic data structure representing a set of content items that should not be provided to a user device, content item data for content items available to be provided, and a request to determine whether any content item data is invalid, determining that the content item data for a given content item is invalid because the given content item may be in the set of content items represented by the probabilistic data structure, including removing the content item data for the given content item that was determined to be invalid; and preventing distribution of content items including the given content item.

Abstract: Methods, apparatuses, and systems for improving data mapping are provided. An example method may include retrieving a first plurality of data objects associated with a first database schema from a database, determining a first data classifier corresponding to the first database schema, generating a mapping specification based at least in part on the first data classifier and the first plurality of data objects, and generating a second plurality of data objects based at least in part on the first plurality of data objects and the mapping specification.

Abstract: A system and method for providing governed search capabilities using generative artificial intelligence (AI) and large language learning models to permit users to parse and search through and to generate detailed content from both structured and unstructured datasets.

Abstract: Provided is a method for an electronic device to perform inference based on encrypted data received from an external device, using an artificial intelligence (AI) model, the method including: transforming the AI model to perform inference based on encrypted data, generating parameter information including information about at least one parameter for encrypting data to be input to the AI model, based on the transformed AI model, transmitting the parameter information to the external device, receiving, from the external device, data encrypted based on the parameter information, and obtaining an inference result output from the transformed AI model by inputting the encrypted data to the transformed AI model.

Abstract: Some techniques described herein relate to utilizing a machine-learning (ML) model to select respective samples for queries of a query sequence. In one example, a method includes receiving a query in a query sequence, where the query is directed toward a dataset. Samples are available as down-sampled versions of the dataset. The method further include applying an agent to select, for the query, a sample from among the samples of the dataset. The agent includes an ML model trained, such as via intent-based reinforcement learning, to select respective samples for queries. The query is then executed against the sample to output a response.

Abstract: A method may include identifying an identifier field included in a first datatype of a seed data sample associated with a source system. The identifier field may store a first value that enables a differentiation between different instances of the first datatype. A relationship field, which stores a second value that define a relationship between the first datatype and a second data type, may be identified. A synthetic data sample may be generated by populating the identifier field of the synthetic data sample with a synthetically generated value and the relationship field of the synthetic data sample with the second value. The synthetic data sample may be sent to a target system to enable a performance of a task at the target system. The synthetic data sample may supplement a volume and/or a diversity of the data that occurs organically at the source system.

Abstract: Systems and methods of the present disclosure may read source data corresponding to a source variable and apply a transformation to the source variable to generate an output variable. Accordingly, one such method comprises receiving a request to access an output variable by applying one or more data transformations to input data; before applying an actual data transformation to the input data, identifying that a match exists with a database of prior data transformations that have been applied to source variables to generate derived variables; identifying that a source variable in the matching one or more data transformations is flagged as sensitive data; and processing the request to access the output variable subject to an access restriction in response to the identification of the source variable that is flagged as sensitive data.

Abstract: An original text that is a representation of a narration of a patient encounter provided by a clinician may be received and re-formatted to produce a formatted text. One or more clinical facts may be extracted from the formatted text. A first fact of the clinical facts may be extracted from a first portion of the formatted text, and the first portion of the formatted text may be a formatted version of a first portion of the original text. A linkage may be maintained between the first fact and the first portion of the original text.

Abstract: As described herein, a system, method, and computer program are provided for synthesizing user transactional data for de-identifying sensitive information. In use, transactional data of a plurality of users is identified. Additionally, the plurality of users are clustered based on the transactional data, to form groups of users having transactional data representing similar transactional behavior. Further, synthesized transactional data is generated for the users in each group by: identifying a subset of the transactional data that corresponds to the users in each group, shuffling the transactional data in the subset across the users in each group, and perturbing portions of the shuffled transactional data.

Abstract: Disclosed herein is new approach for structuring an organization's data, involving at a high level establishing a digital context and populating the digital context with digital content to thereby form what is referred to herein as a digital duplicate. In one aspect, the disclosed approach may be embodied in a computer-implemented method that involves: establishing a data structure comprising (i) a structural context that has at least one data component, where each component of the structural context has associated therewith one or more respective data properties (ii) a semantic context that has at least two data types that further describe individual data properties and; and populating underlying data into an instance of the data structure such that underlying data populated into each respective property of the at least one data component has each of the at least two data types of the semantic context.

Abstract: A machine reading system is described herein that includes a framework in which grammar rules can be developed using a concise language that combines syntax and semantics. The resulting technology thus reduces the development time for new grammars in a new domain. An enormous amount of information appears in the form of natural language across millions of academic papers and other literature sources. For example, in the biological domain, there is a tremendous ongoing effort to extract individual chemical interactions from these texts, but these interactions are only isolated fragments of larger causal mechanisms such as protein signaling pathways. The proposed rule-based event extraction framework can model underlying syntactic representations of events in order to extract signaling pathway fragments. Though application to the biomedical domain is herein described, the framework is domain-independent and is expressive enough to capture most complex events annotated by domain experts.

Abstract: Techniques for schema mismatch detection and evolution are described. When data is being uploaded into a source table, schema of the data to be uploaded can be compared with the schema for the source table. If a schema mismatch is detected, the schema of the source table can be modified, and the upload can be continued without data loss.

Abstract: A method, system, and computer program product for identifying reference data values in a source data set. The method may include inputting a block of attribute values to a predefined machine learning model. The method may also include receiving an indication of a presentation layout of the block of the attribute values and an associated reference data extraction method. The method may also include determining a reading direction of the block of values. The method may also include identifying one or more inspection areas in the reading direction of the block of values. The method may also include determining sets of the one or more inspection areas that share a common presentation feature. The method may also include identifying tokens in an inspection area. The method may also include determining if the inspection area includes reference data values. The method may also include outputting the reference data values.

Abstract: A method and system for automatically generating a data determining result includes an acquisition end sending a data acquisition module to a plurality of user terminals, and sending the first data fed back by the user terminals on the data acquisition module to an analysis end. The analysis end generates a display manner of the data determination result on the basis of a configuration condition. The display manner includes analysis items, a first operation logic for determining analysis values of the analysis items and/or a second operation logic for determining cross analysis values of different analysis items. The analysis end determines the analysis value of the analysis items and/or the cross analysis value of the different analysis items in the display manner from the first data, and generates a data determination result.

Abstract: The subject technology receives, from a document object model, a set of nodes of a web page, each node corresponding to an element of the web page. The subject technology determines, using an intersection observer, a position and visibility of a set of first elements within a viewport of the web page. The subject technology determines, using a mutation observer, a set of new elements within the viewport that was previously hidden and now visible. The subject technology provides a set of visible elements, based at least in part on the set of new elements and the set of first element, as elements that have been seen by a user during a session. The subject technology generates an index based at least in part on the set of visible elements, the index comprising information corresponding to text that was visible to the user during the session.

Abstract: Systems, method sand computer readable medium are provided for proving real-time Self Optimizing Network (SON) Virtual Network Function (VRF) included as part of the HNG. In one embodiment data from connected devices is forwarded to the HNG; the data is organized into virtualized containers; and the data is processed by agile analytics and results are displayed to a user.

Abstract: A method for configuring an observable object as a digital twin in a digital twin system of any one domain is provided. The method for configuring a digital twin includes defining a purpose for expressing the observable as a digital twin in the domain, organizing data based on a role of the observable object in the domain, configuring the observable object into the digital twin based on the data for the purpose, and synchronizing the observable object and the digital twin.

Abstract: In an exemplary implementation, systems, devices and methods for generating a common data engineering framework include receiving source data having one or more formats from an external source, analyzing the source data, generating a data dictionary having a mapping of data elements of the source data based on the analysis of the source data, generating and storing in memory a configuration file having the data dictionary, and generating and storing in the configuration file a data model logically organizing the data from the data dictionary in a common format.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for responding to a query. In some implementations, a computer obtains a query. The computer determines a meaning for each term in the query. The computer determines user data for the user that submitted the query. The computer identifies one or more ontologies based on the meanings for at least some of the terms. The computer identifies a knowledge graph based on the identified ontologies and the user data. The computer generates a response to the query by traversing a path of the identified knowledge graph to identify items in the knowledge graph based on the determined meaning for each of the terms. The computer generates path data that represents the path taken by the computer through the identified knowledge graph. The computer provides the generated response and the path data to the client device.

Abstract: Techniques are disclosed relating to increasing the amount of training data available to machine learning algorithms. A computer system may access an initial set of training data that specifies a plurality of sequences, each of which may define a set of data values. The computer system may amplify the initial set of training data to create a revised set of training data. The amplifying may include identifying sub-sequences of data values in ones of the plurality of sequences in the initial set of training data and using an inheritance algorithm to create a set of additional sequences of data values, where each one of the set of additional sequences may include sub-sequences of data values from at least two different sequences in the initial set of training data. The computer system may process the set of additional sequences using the machine learning algorithm to train a machine learning model.

Abstract: A method for capturing a screen includes obtaining a display device list. The display device list at least includes a physical display device and a first category of virtual display device. It is identified whether a value of a flag bit of the first category of virtual display device is a preset value. In response to the value of the flag bit of the first category of virtual display device being the preset value, a video merging result of the physical display device as the screen capturing result.

Abstract: The present invention provides a vehicle control apparatus, a vehicle control method, and a vehicle control system capable of improving reliability of road recognition.

Abstract: Methods, computer program products, and systems are presented. The method computer program products, and systems can include, for instance: obtaining a query statement; parsing the query statement and determining from the parsing that the query statement is a compound predicate query statement that includes a first predicate and a second predicate; responsively to the parsing, rewriting the obtained query statement to provide a transformed query statement, wherein the rewriting includes (a) specifying generating of a temporary table, wherein the specified generating uses data values of the first predicate and (b) specifying a join function that uses the temporary table and a table referenced in the query statement; evaluating a candidate access path associated to the transformed query statement; selecting the candidate access path as an access path for execution; and executing the transformed query statement according to the selected candidate access path for execution.

Abstract: In general, embodiments of the technology relate to a method and system for implementing external content type (ECT) object types in a content management system (CMS). More specifically, embodiments of the technology relate to augmenting the object model with a new class of object type—an external content type (ECT) object type. The ECT object types are a class of object types that are used to manage object (referred to as managed external objects) that are located in external data systems.

Abstract: A user interface displays: a first column comprising non-editable input strings retrieved from a data field; a second column comprising editable output strings initialized from the data field; and an expression window displaying a transformation function ƒ. The computer iteratively processes user inputs, each user input i providing a sample row transformation to edit an ith output string ti. Some user inputs i designate a contiguous substring ssi of the corresponding input string si. The contiguous substring expresses a causal basis for transforming the input string si. into the output string ti. The computer updates the transformation function ƒ according to the provided sample row transformations so that: ƒ(s1)=t1, . . . , ƒ(si)=ti; the transformation function ƒ specifies text or string position of at least one contiguous substring; and ƒ has minimal branching among possible transformation functions that satisfy the samples.

Abstract: A system for identifying items based on aggregated metadata obtains images of an item. The system extracts a set of features from images of the item. The system identifies a first value of a first feature associated with a first image of the item. The system identifies a second value of the first feature associated with a second image of the item. The system aggregates the first value and the second value. The system associates the item to the aggregated first value and the second value, where the aggregated first value and the second value represent the first feature of the item. The system adds a new entry for each image of the item to a training dataset associated with an item identification model.

Abstract: An illustrative embodiment disclosed herein is a computer-implemented method. In some embodiments, the method includes uploading, by a processor, an object to a source bucket in an object store and creating, by the processor, a lambda bucket in the object store that is symlinked to the source bucket. In some embodiments, the lambda bucket is associated with a transformation function. In some embodiments, the method includes associating, by the processor, a lambda function with the object in the source bucket, receiving, by the processor, a request to download the object from the lambda bucket, detecting, by the processor, that the object is in the source bucket and associated with the lambda function, fetching, by the processor, the object from the source bucket, generating, by the processor, a transformed object by invoking the lambda function and the transformation function on the object, and downloading, by the processor, the transformed object.

Abstract: A configuration system may generate synthetic datasets based on machine learning, and may provide the synthetic datasets in order to configure, train, test, and/or otherwise modify operation of network equipment and/or other devices. The configuration system may receive a sampling of data used by the particular network equipment, and may determine parameters that define values for the variables represented in the sampling. The configuration system may generate different datasets based on the parameters, and may determine an accuracy of each dataset to the sampling based on the values of each dataset conforming by different amounts to the parameters that define the values for the variables represented in the sampling by different amounts. The configuration system may modify the network equipment operation using the values from a particular dataset in response to determining that the accuracy of the particular dataset is greatest to the sampling.

Abstract: Various embodiments provide methods, systems, apparatus, computer program products, and/or the like for managing, ingesting, monitoring, updating, and/or extracting/retrieving information/data associated with an electronic record (ER) stored in an ER data store and/or accessing information/data from the ER data store, wherein the ERs are generated, updated/modified, and/or accessed via a graph-based domain ontology.

Abstract: Methods and systems for performing a sequence of machine learning tasks. One system includes a sequence of deep neural networks (DNNs), including: a first DNN corresponding to a first machine learning task, wherein the first DNN comprises a first plurality of indexed layers, and each layer in the first plurality of indexed layers is configured to receive a respective layer input and process the layer input to generate a respective layer output; and one or more subsequent DNNs corresponding to one or more respective machine learning tasks, wherein each subsequent DNN comprises a respective plurality of indexed layers, and each layer in a respective plurality of indexed layers with index greater than one receives input from a preceding layer of the respective subsequent DNN, and one or more preceding layers of respective preceding DNNs, wherein a preceding layer is a layer whose index is one less than the current index.

Abstract: An automated method for search and generation of relevant search results includes: receiving both discrete parameters and freeform search characters input by a user; simultaneously searching a first freeform entry source and a second freeform entry source to obtain corresponding first and second suggested freeform entries; ranking the first suggested freeform entries according to first rules of relevancy to the user, and generating first ranked suggested freeform entries; ranking the second suggested freeform entries according to second rules of relevancy to the user, and generating second ranked suggested freeform entries; combining the first and second ranked freeform entries into a combined set of suggested freeform entries, and ranking the combined set of suggested freeform entries according to combined rules of relevancy to the user, and generating combined ranked suggested freeform entries; and transmitting the combined ranked suggested freeform entries to the user for selection of a desired type ahead

Abstract: Disclosed is a social networking system which provides personal pages for users and enables other users to modify the personal page of a given user. Generally, a given webpage is enabled to be collaboratively modified and edited by multiple users. In some embodiments, the owner of the personal page approves the trusted users and may define one or more rules that automatically define a trusted user. In some embodiments, rules for modifying/editing a social webpage may specify the type of operations users can perform on the page and may specify one or more potential target actions.

Abstract: Aspects of the present disclosure relate to converting between structured and tabular data formats. Data can be received in a tabular format. An array can be built for each of a plurality of objects within the data in the tabular format, each object corresponding to at least one identified header of the identified headers. A data row can be parsed using at least one of the built arrays and data within the data row can be added to the structured format in a specific location based characteristics indicated in the at least one array. Data can also be converted from the structured format into the tabular format using the built arrays.

Abstract: Techniques for schema mismatch detection and evolution are described. When data is being uploaded into a source table, schema of the data to be uploaded can be compared with the schema for the source table. If a schema mismatch is detected, the schema of the source table can be modified, and the upload can be continued without data loss.

Abstract: The system receives a request associated with a database including a query and a type associated with the database. The request is expressed in a language not native to the database. The query includes a command in the language not native to the database. The type associated with the database can include an in-memory database, a NoSQL database, or a relational database. Based on the type associated with the database, the system can create a corresponding command native to the database by translating the query to the corresponding command native to the database. The system can connect to the database, execute the corresponding command native to the database, and receive an output of the corresponding command native to the database. The system can translate the output of the corresponding command native to the database into the language not native to the database and provide the translated output to the user.

Abstract: A vehicle data management device includes a data acquisition unit and a data conversion unit. The data acquisition unit is configured to acquire, from each of a plurality of in-vehicle devices mounted on a corresponding vehicle of a plurality of vehicles, a vehicle identification information that is set based on at least a model of the vehicle, and a first data that is contained in a communication frame and is indicative of a vehicle information. The data conversion unit is configured to convert the first data into a label and a second data based on a conversion map that contains a label identification information for identifying the label corresponding to the first data, and a conversion information for converting the first data into the second data. The conversion map contains at least one label identification information and at least one conversion information for each vehicle identification information.

Abstract: A method for an interpreter framework is described. The method includes an interpreter module of a computing system receiving a first data file that includes multiple data sections. The interpreter module identifies each of the data sections of the first data file. Each data section includes data representing a real-world entity. For each respective data section of the first data file, the interpreter module parses information about the real-world entity represented by the data included in the data section. The interpreter module generates a second data file based on the parsed information for each of the data sections. The second data file includes encoded data for rendering a digital geographic structure using information about the real-world entity.

Abstract: The present invention relates to execution optimization of database queries. Herein are techniques for optimal execution based on query interpretation by translation to a domain specific language (DSL), with optimizations such as partial evaluation, abstract syntax tree (AST) rewriting, just in time (JIT) compilation, dynamic profiling, speculative logic, and Futamura projection. In an embodiment, a database management system (DBMS) that is hosted on a computer generates a query tree that represents a database query that contains an expression that is represented by a subtree of the query tree. The DBMS generates a sequence of DSL instructions that represents the subtree. The sequence of DSL instructions is executed to evaluate the expression during execution of the database query. In an embodiment, an AST is generated from the sequence of DSL instructions. In an embodiment, the DSL AST is optimally rewritten based on a runtime feedback loop that includes dynamic profiling information.

Abstract: Systems, methods and computer-readable media are provided for facilitating patient health care by providing discovery, validation, and quality assurance of nomenclatural linkages between pairs of terms or combinations of terms in databases extant on multiple different health information systems that do not share a set of unified codesets, nomenclatures, or ontologies, or that may in part rely upon unstructured free-text narrative content instead of codes or standardized tags. Embodiments discover semantic structures existing naturally in documents and records, including relationships of synonymy and polysemy between terms arising from disparate processes, and maintained by different information systems. In some embodiments, this process is facilitated by applying Latent Semantic Analysis in concert with decision-tree induction and similarity metrics.

Abstract: The present disclosure provides methods and systems for performing licence plate matching. A reference list comprising licence plate identifiers is obtained. At least one transformation rule is applied to the licence plate identifiers of the reference list to generate entries forming at least one augmented reference list, and storing the at least one augmented reference list in at least one database. A search request comprising an input licence plate identifier is obtained. The at least one transformation rule is applied to the input licence plate identifier to generate a search list comprising at least one search term. The at least one database is queried with the search list to identify a match between at least one of the entries of the at least one augmented reference list and the at least one search term of the search list. A signal responsive is output to identifying the match.

Abstract: A memory sub-system performs a first media management operation among a plurality of individual data units of a memory device after a first interval, the first media management operation comprising a first algebraic mapping function, and performs a second media management operation among a first plurality of groups of data units of the memory device after a second interval, wherein a first group of the first plurality of groups comprises the plurality of individual data units, the second media management operation comprising a second algebraic mapping function.

Abstract: The present disclosure provides an experimentation framework for a computational environment in a distributed system. A machine-learning model may be created that predicts at least one output produced by the computational environment based on at least one input provided to the computational environment. During an evaluation time period that is subsequent to at least one modification being made to the computational environment, at least one modified output produced by the computational environment may be determined. The machine-learning model may be used to calculate at least one predicted output that would have been produced by the computational environment during the evaluation time period if the at least one modification had not been made. A determination may also be made about how the at least one modification affected the computational environment based on a comparison of the at least one modified output and the at least one predicted output.

Abstract: The invention provides a neural network quantization method and device and a related product. The neural network quantization method is used for quantizing data of a computation layer of a neural network. The technical scheme provided by the invention has the advantage of low cost.

Abstract: Systems and methods herein describe accessing a data processing pipeline, causing presentation of the data processing pipeline on a graphical user interface of a computing device; receiving a selection of a first user interface element within the graphical user interface, generating a datafile representing the data processing pipeline, submitting the datafile and an application to a software framework using an application programming interface, receiving, from the application, the generated datasets, applying the data operations the data processing pipeline, collecting performance data metrics from the data processing pipeline, and dynamically updating the graphical user interface with the collected performance data metrics.