This disclosure provides a pari-mutuel wagering system that includes a first wagering facility communicably coupled with a network and operable to receive a bet on a wagering event hosted by a second wagering facility. The first wagering facility is further operable to transmit the bet to the second wagering facility via the network. The system further includes a clearinghouse communicably coupled with the network and operable to capture audit information associated with the bet from the network.

Embodiments of the present disclosure are directed to methods, computer program products, and computer systems of a robotic apparatus with robotic instructions replicating a food preparation recipe. In one embodiment, a robotic control platform, comprises one or more sensors; a mechanical robotic structure including one or more end effectors, and one or more robotic arms; an electronic library database of minimanipulations; a robotic planning module configured for real-time planning and adjustment based at least in part on the sensor data received from the one or more sensors in an electronic multi-stage process file, the electronic multi-stage process recipe file including a sequence of minimanipulations and associated timing data; a robotic interpreter module configured for reading the minimanipulation steps from the minimanipulation library and converting to a machine code; and a robotic execution module configured for executing the minimanipulation steps by the robotic platform to accomplish a functional result.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for retrieving and using contextual data from previous conversation sessions in conversational searches. In one aspect, a method includes receiving a first query for a first user session, determining that the first query refers to one or more tags in a first repository, the first repository associating respective identifiers to respective tags, each identifier representing a corresponding user session, determining one or more particular identifiers associated with the one or more tags in the first repository, retrieving particular contextual data associated with the determined particular identifiers in a second repository, the second repository associating respective identifiers to respective contextual data associated with corresponding user sessions represented by the respective identifiers, and performing an action responsive to the first query based on the retrieved particular contextual data.

A bipartite application implements a table auto-completion (TAC) algorithm on the client side and the server side. A client module runs a local model of the TAC algorithm on a user device and a server module runs a global model of the TAC algorithm on a server machine. The local model is continuously adapted through on-the-fly training, with as few as a negative example, to perform TAC on the client side, one document at a time. Knowledge thus learned by the local model is used to improve the global model on the server side. The global model can be utilized to automatically and intelligently extract table information from a large number of documents with significantly improved accuracy, requiring minimal human intervention even on complex tables.

Embodiments of the present disclosure provide for improved mapping from sequential semi-structured data of a first custom data format to a second custom data format. The improved mechanism for mapping between custom data formats significantly reducing the amount of manual effort otherwise used for creating mapping rules. Example embodiments utilize a state extractor model that extracts an untrained modified finite state machine embodied by a skeleton set of extracted states from particular sequential semi-structured input data, and generate a trained modified finite state machine that maps the first custom data format to the second custom data format based at least in part on the untrained modified finite state machine, sequential semi-structured input data of the first custom data format, and database structured output data of the second custom data format. The trained modified finite state machine may be used for subsequent processing data of the first custom data format.

Computing systems, computing apparatuses, computing methods, and computer program products are disclosed for machine learning model lifecycle management. An example computing method includes receiving a machine learning model selection, a machine learning model experiment creation input, a machine learning model experiment run type, and a machine learning model input data path. The example method further includes determining a machine learning model execution engine based on the machine learning model experiment creation input and the machine learning model experiment run type. The example method further includes retrieving input data based on the machine learning model input data path. The example method further includes executing a machine learning model experiment based on the machine learning model execution engine, machine learning model experiment creation input, and the input data. The example method further includes generating one or more machine learning model scores based on the machine learning model experiment.

The present disclosure provides a method, system, and device for security object synchronization at multiple nodes of an active-active environment. To illustrate, a source node may generate a corresponding security object sync request for each of multiple target nodes. The source node may send the security object sync request to the target nodes via a source queue and, for each target node, a corresponding distribution queue. A distribution queue may be closed based on an acknowledgement received from a corresponding target node, after a time period, or after a number of transmission attempts. A synchronization log may be maintained to indicate which security object sync requests have been delivered to which target nodes. In some implementations, the source node and the target nodes are part of an active-active environment that may be synchronized in time so the nodes resolve conflicts between received security object updates initiated from two different nodes.

A method, system and computer program product, the method comprising: obtaining a first model trained upon cases and labels, the first model providing a prediction in response to an input case; obtaining a second model trained using the cases and indications whether a predictions of the first model are correct, the second model providing a correctness prediction for the first; determining a case for which the second model predicts that the first provides an incorrect prediction; further training the first model also on a first corpus including the case and a label, thereby improving performance of the first model; providing the case to the first model to obtain a first prediction; and further training the second model also on a second corpus including the case and a correctness label, the correctness label being “correct” if the first prediction is equal to the label, thereby improving performance of the second model.

In some implementations, a UAV flight system can dynamically adjust UAV flight operations based on radio frequency (RF) signal data. For example, the flight system can determine an initial flight plan for inspecting a RF transmitter and configure a UAV to perform an aerial inspection of the RF transmitter. Once airborne, the UAV can collect RF signal data and the flight system can automatically adjust the flight plan to avoid RF signal interference and/or damage to the UAV based on the collected RF signal data. In some implementations, the UAV can collect RF signal data and generate a three-dimensional received signal strength map that describes the received signal strength at various locations within a volumetric area around the RF transmitter. In some implementations, the UAV can collect RF signal data and determine whether a RF signal transmitter is properly aligned.

One embodiment provides a method, including: receiving a process flow diagram element of a process flow diagram; identifying a context of the process flow diagram element, wherein the identifying a context comprises identifying (i) categories of elements connected to the process flow diagram element, (ii) swimlanes within the process flow diagram, and (iii) text included in the process flow diagram; encoding features of the process flow diagram element into a semantic vector, wherein the features are identified from the context of the process flow diagram element; and predicting, utilizing a process flow diagram model, a process flow diagram element for the process flow diagram based upon the at least one process flow diagram element, wherein the process flow diagram model receives and analyzes the features of the at least one process flow diagram and outputs the predicted process flow diagram element.