Abstract: In some examples, a system can access video data collected from one or more image sensors, the video data showing a region of interest proximate to a machine. The system can execute an object detection model to detect that a person is within the region of interest proximate to the machine based on the video data. The system can detect a motion status of a component of the machine. The system can execute a pose estimation model on the video data to estimate a pose of the person with respect to the machine. The system can detect a safety rule violation based on the pose of the person with respect to the machine, and the motion status of the machine. The system can transmit a signal to a controller of the machine in response to detecting the safety rule violation.

Abstract: A system and method include creating a project package for an Event Stream Processing (ESP) project, generating a first manifest file from the project package, creating a first container pod on a cluster based on the first manifest file, executing a container file generator software and a build kit software on the first container pod, executing an ESP server on the container file generator software, executing the ESP project on the ESP server such that data is not streaming to the ESP server, identifying a list of required software components needed to execute the ESP project, creating a container file having a subset of software components based on the list of required software components, generating a ESP project container image for the ESP server based on the container file, and deploying the ESP project using the ESP project container image to analyze data streamed to the ESP project.

Abstract: A computer-implemented system, computer-implemented method, and computer-program product includes receiving a natural language query from a user for executing an analytical task; generating an analytical large language model (LLM) prompt based on the natural language query and, in response to generating the analytical LLM prompt, orchestrating an LLM-directed workflow for handling the natural language query by: automatically prompting, using the analytical LLM prompt, an analytical task-oriented LLM to generate a structured query for querying a data catalog application; querying the data catalog application using the structured query generated by the analytical task-oriented LLM; obtaining query results from the data catalog application, where the query results include metadata associated with at least one element accessible to the data catalog application; prompting the analytical task-oriented LLM to identify a given analytical task associated with a given analytical agent; and automatically executing, by t

Abstract: A system and method include breaking symmetry in a query graph by converting the query graph into a transformed query graph by generating a symmetry breaking expression that includes detecting one or more orbits in the transformed query graph, selecting an orbit from the one or more orbits having more than one node, generating an automorphism breaking sub-expression for the selected orbit, assigning a node of the selected orbit a unique node attribute, recalculating the one or more orbits in the transformed query graph, repeating the process until each node is in its own orbit, and combining each of the automorphism breaking sub-expressions to obtain the symmetry breaking expression. Using the symmetry breaking expression, the system and method include finding one or more subgraphs of a main graph that match the symmetry breaking expression of the query graph.

Abstract: In one example, a computer system can generate a graphical user interface (GUI) for forecasting software including a drag-and-drop canvas with a set of rearrangeable nodes defining a forecasting pipeline. The computer system can detect a user interaction for attaching an external-language execution node to the pipeline, which can be used to insert custom code defined using an external programming language. The computer system can receive the custom code. The computer system can receive a user input to initiate execution of the pipeline. The computer system can generate wrapped custom code by augmenting the custom code with additional program code including shared variables. The computer system can provide the wrapped custom code to a set of execution threads configured to execute the wrapped custom code as part of the pipeline to generate one or more forecasts. The computer system can output the forecasts in the GUI.

Abstract: The computing device trains a first model on a first data set using a first graph to predict relevant links between a plurality of nodes. The computing device obtains the first data set or a second data set associated with the plurality of nodes. The computing device determines the one or more features for the one or more links between the plurality of nodes, applies the trained first model to the one or more links between the plurality of nodes, outputs the relevant links and non-relevant links of the one or more links between the plurality of nodes, removes the non-relevant links between the plurality of nodes, connects each node of the plurality of nodes with the relevant links to generate one or more first sets of networks, and outputs the one or more first sets of generated networks.

Abstract: A system, method, and computer-program product includes receiving, by a controller node, a request to execute a client process associated with a first programming language and a plurality of threads; launching, by the controller node, a plurality of multi-language worker processes based on a number of threads associated with the client process; and instructing, by the controller node, the plurality of multi-language worker processes to execute the plurality of threads associated with the client process.

Abstract: The computing device trains a first model on a first data set using a first graph to predict relevant links between a plurality of nodes. The computing device applies the trained first model to the one or more links between the plurality of nodes from a first node, iteratively connects each node to the one or more first sets of generated networks for each of the relevant links until the relevant links for connection to the plurality of nodes are not present, and outputs the one or more first sets of generated networks. The computing device also applies the trained first model to the one or more links between the plurality of nodes, removes the non-relevant links, connects each node of the plurality of nodes with the relevant links to generate one or more second sets of networks, and outputs the one or more second sets of generated networks.

Abstract: A system, method, and computer-program product includes identifying a plurality of code synthesis items for a target programming language, generating a code synthesis prompt based on a first sampling of the plurality of code synthesis items, synthesizing, via a large language model, a plurality of raw code segments using the code synthesis prompt, executing the plurality of raw code segments with a code interpreter associated with the target programming language, determining one or more valid code segments of the plurality of raw code segments that the code interpreter successfully executed, aggregating, via a second sampling, the one or more valid code segments into one or more validated code synthesis training samples, and training a code generation model using the one or more validated code synthesis training samples. User interfaces may be provided to allow target coding tasks to be specified via text or speech.

Abstract: A computer-implemented system includes identifying a target hierarchical taxonomy comprising a plurality of distinct hierarchical taxonomy categories; extracting a plurality of distinct taxonomy tokens from the plurality of distinct hierarchical taxonomy categories; computing a taxonomy vector corpus based on the plurality of distinct taxonomy tokens; computing a plurality of distinct taxonomy clusters based on an input of the taxonomy vector corpus; constructing a hierarchical taxonomy classifier based on the plurality of distinct taxonomy clusters; converting a volume of unlabeled structured datasets to a plurality of distinct corpora of taxonomy-labeled structured datasets based on the hierarchical taxonomy classifier; and outputting at least one corpus of taxonomy-labeled structured datasets of the plurality of distinct corpora of taxonomy-labeled structured datasets based on an input of a data classification query.

Abstract: A system, method, and computer-program product includes commencing a parent computer process based on receiving a request to perform an analytical operation on one or more datasets, commencing at least one child computer process that is launched by the parent computer process when the parent computer process initiates an execution of the analytical operation on the one or more datasets, transmitting, by the at least one child computer process, a request to the parent computer process to retrieve the one or more datasets, writing, by the parent computer process, the one or more datasets to a cross-process queue based on the parent computer process receiving the requests, reading, by the at least one child computer process, the one or more datasets from the cross-process queue, and executing, using an analytical application executing on the least one child computer process, the analytical operation based on the one or more datasets.

Abstract: A system, method, and computer-program product includes commencing a parent computer process based on receiving a request to perform an analytical operation on one or more datasets, commencing at least one child computer process that is launched by the parent computer process when the parent computer process initiates an execution of the analytical operation on the one or more datasets, transmitting, by the at least one child computer process, a request to the parent computer process to retrieve the one or more datasets, writing, by the parent computer process, the one or more datasets to a cross-process queue based on the parent computer process receiving the requests, reading, by the at least one child computer process, the one or more datasets from the cross-process queue, and executing, using an analytical application executing on the least one child computer process, the analytical operation based on the one or more datasets.

Abstract: A system, method, and computer-program product includes obtaining a decisioning dataset comprising a plurality of favorable decisioning records and at least one unfavorable decisioning record; detecting, via a machine learning algorithm, a favorable decisioning record of the plurality of favorable decisioning records that has a vector value closest to a vector value of the unfavorable decisioning record; executing a counterfactual assessment between the favorable decisioning record and the unfavorable decisioning record; generating an explainability artifact based on one or more bias intensity metrics to explain a bias in a machine learning-based decisioning model; and in response to generating the explainability artifact, displaying the explainability artifact in a user interface.

Abstract: A system, method, and computer-program product includes obtaining a decisioning dataset comprising a plurality of favorable decisioning records and at least one unfavorable decisioning record; detecting, via a machine learning algorithm, a favorable decisioning record of the plurality of favorable decisioning records that has a vector value closest to a vector value of the unfavorable decisioning record; executing a counterfactual assessment between the favorable decisioning record and the unfavorable decisioning record; generating an explainability artifact based on one or more bias intensity metrics to explain a bias in a machine learning-based decisioning model; and in response to generating the explainability artifact, displaying the explainability artifact in a user interface.

Abstract: In one example, a computer system can generate a graphical user interface (GUI) for forecasting software including a drag-and-drop canvas with a set of rearrangeable nodes defining a forecasting pipeline. The computer system can detect a user interaction for attaching an external-language execution node to the pipeline, which can be used to insert custom code defined using an external programming language. The computer system can receive the custom code. The computer system can receive a user input to initiate execution of the pipeline. The computer system can generate wrapped custom code by augmenting the custom code with additional program code including shared variables. The computer system can provide the wrapped custom code to a set of execution threads configured to execute the wrapped custom code as part of the pipeline to generate one or more forecasts. The computer system can output the forecasts in the GUI.

Abstract: A system, method, and computer-program product includes selecting, by a controller node, a plurality of hyperparameter search points from a hyperparameter search space; instructing, by the controller node, one or more worker nodes to concurrently train a plurality of machine learning models for a target number of epochs using the plurality of hyperparameter search points; receiving, from the one or more worker nodes, a plurality of performance metrics that measure a performance of the plurality of machine learning models during the target number of epochs; and removing, by the controller node, one or more underperforming hyperparameter search points from the plurality of hyperparameter search points according to a pre-defined performance metric ranking criterion associated with the plurality of performance metrics.

Abstract: A system, method, and computer-program product includes implementing a cross-process queue within a single computer that is configured to transfer a data block between an operating system process executing a write operation and an operating system process executing a read operation, initializing in-memory cell indices within the cross-process queue that include a write operation index tracking index values of one or more cells within the cross-process queue that are available to write and a read operation index tracking index values of one or more cells within the cross-process queue that are available to read, and implementing a cell synchronization data structure tracking states of a plurality of cells of the index of cells of the cross-process queue.

Abstract: A computer-implemented method includes receiving a natural language input including a natural language request for executing an analytical task and processing the natural language input by a language model, where the processing may include translating the natural language input to an analytical function call for calling an analytical function of a set of distinct analytical functions of an analytics computing server. Additionally, the computer-implemented method includes calling the analytical function at the analytics computing server using the analytical function call, receiving a technical output in response to calling the analytical function, and outputting a response to the natural language input that includes the technical analytical output.

Abstract: A system, method, and computer-program product includes implementing a cross-process queue within a single computer that is configured to transfer a data block between an operating system process executing a write operation and an operating system process executing a read operation, initializing in-memory cell indices within the cross-process queue that include a write operation index tracking index values of one or more cells within the cross-process queue that are available to write and a read operation index tracking index values of one or more cells within the cross-process queue that are available to read, and implementing a cell synchronization data structure tracking states of a plurality of cells of the index of cells of the cross-process queue.

Abstract: In one example, a system can receive information about a tabular data structure in a memory including a set of data and a first memory allocation. The system can determine a type of the tabular data structure, the type selected from among two types including a native type and a non-native type. The system can, in response to the type being the native type, identify a first proxy data table usable as a proxy for the tabular data structure that shares the first memory allocation. The system can receive a first indication to access the set of data from application code. The system can issue one or more first read commands to the first proxy data table to cause the set of data to be read from the tabular data structure.