Abstract: Data processing apparatus comprises a processing module to initiate data handling transactions, for transmission to a data handling module by a transaction interface, in response to successive processing instructions; a verification module connectable to the transaction interface and configured to detect test data, representing an ordered series of communications via the transaction interface generated in response to a test series of processing instructions; in which the verification module is configured to compare two or more instances of the test data generated in response to the same test series of processing instructions and to detect whether the two or more instances of the test data are identical.

Abstract: An example operation includes one or more of determining a predictive task and one or more constraints of the predictive task based on inputs to a graphical user interface of a software application, executing a machine learning model on input data including the predictive task and the one or more constraints to select a LLM from among a plurality of available LLMs for executing the predictive task, additionally executing the machine learning model on the LLM and the input data to determine a plurality of components of the predictive pipeline including the LLM, and instantiating an instance of the predictive pipeline including the plurality of components and the LLM via the software application.

Abstract: A computer-implemented method for transforming data is provided. A processor set receives a number of data pairs. The processor set creates a program graph for each data pair in the number of data pairs. The processor set identifies a number of paths between nodes in each program graph. The processor set identifies a number of common paths from the number of paths based on common characters between the input data and the output data for each data pair. The processor set identifies a set of nodes in the program graphs based on the number of common paths. The processor set generates a prompt for a large language model based on the number of data pairs and the set of nodes that represent positions of unmatched characters between input data and output data in the number of data pairs.

Abstract: A computer-implemented method for managing data is provided. A processor set receives a number of data pairs. The processor set annotates content of input data from the number of data pairs based on common entities associated with items in the content of input data from the number of data pairs using a large language model. The processor set identifies a set of common entities from the common entities associated with the content in input data from the number of data pairs using the large language model. The processor set generates a list of items for each common entity from the set of common entities using the large language model. The processor set stores the lists of items and output data in the number of data pairs in a program cache.

Abstract: In an approach to improve knowledge content query generation, embodiments identify one or more drift points based on implicit user feedback-based assessment and a user experience to generate one or more knowledge content queries of different complexity from a multi-media file. Further, embodiments adjust a threshold for the one or more drift points to generate different variations of the one or more knowledge content queries and perform iterative refinement on the one or more knowledge queries based on one or more previous iteration evaluations. Additionally, embodiments generate the one or more knowledge content queries in different modalities based on the multi-media file and the threshold for the one or more drift points.

Abstract: A method for dynamic metadata delivery in a content streaming service. The method may comprise receiving, at a computing system and from a client device, a request for a manifest, the manifest comprising links to one or more respective data files. The computing system may access metadata and the links to one or more respective datafiles, the metadata associated with at least one of the one or more respective datafiles. The computing system may generating the manifest comprising the links to the one or more respective data files and the metadata and provide the manifest to the client device.

Abstract: A method includes receiving, by a processing device, first data generated by a first sensor of a substrate processing system. The first data is generated responsive to the first sensor receiving electromagnetic radiation from a substrate held by a robot arm of a transfer chamber in the substrate processing system. The method further includes processing the first data to obtain second data. The second data includes a first indication of performance of the substrate processing system. The method further includes causing, in view of the second data, performance of a corrective actions associated with the substrate processing system.

Abstract: The invention provides a compound of formula I and a process for preparing a compound of formula I, wherein R is H (Formula Ia) or CH2COCH3 (Formula Ib).

Abstract: A computer-implemented method, a computer program product, and a computer system for automated model lineage inference. A computer system identifies training datasets which is used to train a machine learning model. A computer system identifies parent datasets from which the training datasets are derived. A computer system identifies associated feature transformations when the training datasets are derived from the parent datasets.

Abstract: Recommending machine learning models is provided. The method comprises training machine learning models, wherein each machine learning model is trained with a unique respective dataset. Metadata associated with each machine learning model is extracted, wherein the metadata includes properties of the respective dataset used to train the machine learning model. The machine learning models and metadata are stored in a model catalog. Upon receiving a new dataset, similarity scores are calculated between the new dataset and the machine learning models in the model catalog according to the properties of the datasets in the metadata of the machine learning models. A closest match machine learning model is identified from the model catalog for the new dataset according to similarity score. Responsive to a determination that the closest match machine learning model exceeds a similarity threshold, predictions for the new dataset are generated with the closest match machine learning model.

Abstract: Implementations relate to techniques for accounting for diversity and/or completeness when generating a long-form natural language response for a search query. Implementations may identify the most relevant passage in a top-ranking documents for the query and then select, from among the most-relevant passages, those passages that meet inclusion criteria, e.g., a minimum relevance to the query, maximizing diversity with other relevant passages, etc. The passages (or portions thereof) that meet the inclusion criteria may be provided with the query to a generative language model, which generates a long-form response to the query. Some implementations may add additional passages to the potential pool of passages, the additional passages identified from top-scoring documents for queries related to the query provided by the user.

Abstract: A system includes a processor that executes computer executable components stored in a memory. The computer executable components can comprise a generation component that generates a GraphQL query. The computer executable components can further comprise a comparison component that compares a predefined GraphQL query to the generated GraphQL query. The computer executable components can further comprise a data mutation component that creates data variations to execute on the predefined GraphQL query and the generated GraphQL query to identify false positives or false negatives of execution equivalence of the queries. The computer executable components can further comprise an evaluation component that compares results of the executed queries to determine sufficiency of the generated GraphQL query.

Abstract: A method, according to one approach, includes: receiving a test sample having schema information and natural language information. The schema information is compared to a pool of entries that correspond to a given query language. One or more entries in the pool that match the schema information of the test sample are identified. One or more entries in the pool that match the natural language information of the test sample are also identified. The method also includes merging selected ones of the entries that match the schema information and selected ones of the entries that match the natural language information. Furthermore, a large language model performs in-context learning using the merged entries and the test sample.

Abstract: A zero trust network access appliance deployed at a customer premises can support gateway and cloud modes. In a gateway mode, the appliance operates as a zero trust network access gateway, and provides zero trust network access to applications hosted at the customer premises, using a firewall at the customer premises for network security. In the cloud mode, the appliance initiates a secure connection with a remote, cloud computing platform that provides a front end for zero trust network access. A threat management facility for the customer provides a control plane for managing zero trust network access provided through the cloud computing platform.

Abstract: The present invention relates to an improved process for the preparation of a compound of formula V or a salt thereof, Formula (V) The present invention also provides a process for the preparation of a compound of formula I, used in the synthesis of the compound of formula V, Formula (I) wherein Formula I is Formula Ia, when R is H wherein Formula I is Formula Ib, when R is CH2COCH3.

Abstract: An embodiment computes a plurality of similarity scores, each similarity score in the plurality of similarity scores measuring a similarity of task data of a first data transformation task to data of a stored data transformation in a plurality of stored data transformations, wherein each of the plurality of stored data transformations comprises a stored data transformation program, each stored data transformation program comprising a data transformation from a first data format to a second data format. An embodiment generates, from a first stored data transformation program in the plurality of stored data transformations, a generated data transformation program. An embodiment performs, by modifying a plurality of records described by the first data transformation task into a second plurality of records according to the generated data transformation program, the first data transformation task.

Abstract: A method for dynamic metadata delivery in a content streaming service. The method may comprise receiving, at a computing system and from a client device, a request for a manifest, the manifest comprising links to one or more respective data files. The computing system may access metadata and the links to one or more respective datafiles, the metadata associated with at least one of the one or more respective datafiles. The computing system may generating the manifest comprising the links to the one or more respective data files and the metadata and provide the manifest to the client device.

Abstract: The present disclosure relates generally to integrated circuits and relates more particularly to circuits, systems, and/or processes for instruction sequence test error tracking.

Abstract: Methods, systems, and computer program products for automatically improving data annotations by processing annotation properties and user feedback are provided herein. A computer-implemented method includes obtaining data annotation pairs, each comprising an input data annotation in a first format and a corresponding output data annotation in a second format; determining, within at least a portion of the data annotation pairs, one or more non-diffs; identifying, across the at least a portion of data annotation pairs, data annotation properties associated with multiple intents by processing the non-diffs using property-related rules; modifying at least a portion of the data annotation pairs based on the identified data annotation properties; outputting the modified data annotation pairs to at least one user; and generating a final collection of data annotation pairs by processing at least a portion of the modified data annotation pairs and user feedback received in response to the outputting.

Abstract: Techniques are disclosed for determining whether to include a bodyprint in a cluster of bodyprints associated with a recognized person. For example, a device performs facial recognition to identify the identity of a first person. The device also identifies and stores physical characteristic information of the first person, the stored information associated with the identity of the first person based on the recognized face. Subsequently, the device receives a second video feed showing an image of a second person whose face is also determined to be recognized by the device. The device then generates a quality score for physical characteristics in the image of the user. The device can then add the image with the physical characteristics to a cluster of images associated with the person if the quality score is above a threshold, or discard the image if not.