Abstract: A method and system learn functions to be associated with data fields of forms to be incorporated into an electronic document preparation system. The functions are essentially sets of operations required to calculate the data field. The method and system receive form data related to a data field that expects data values resulting from performing specific operations. The method and system utilize machine learning and training set data to generate, test, and evaluate candidate functions to determine acceptable functions.

Abstract: An example method comprises receiving first historical data of a first time period and failure data, identifying at least some sensor data that was or potentially was generated during a first failure, removing the at least some sensor data to create filtered historical data, training a classification model using the filtered historical data, the classification model indicating at least one first classified state at a second period of time prior to the first failure indicated by the failure data, applying the classification model to second sensor data to identify a first potential failure state based on the at least one first classified state, the second sensor data being from a subsequent time period, generating an alert if the first potential failure state is identified based on at least a first subset of sensor signals generated during the subsequent time period, and providing the alert.

Abstract: A method includes accessing a subject entity and a subject relation of a focal platform and accessing a knowledge graph representative of control performance data. Further, the method includes computing a set of ranked target entities that cause the subject entity based on the subject relation or are an effect of the subject entity based on the subject relation. Computing the set of ranked target entities is performed using relational hops from the subject entity within the knowledge graph performed using the subject relation and reward functions. The method also includes transmitting the set of ranked target entities to the focal platform. The set of ranked target entities is usable for modifying a user interface of an interactive computing environment provided by the focal platform.

Abstract: A system and method for generating a semantic graph. The method includes: parsing each of a plurality of events into a plurality of objects, wherein the plurality of events includes a plurality of queries, wherein each event of the plurality of events is related to an interaction with at least one data source; determining, for each of the plurality of events, a relationship between two objects of the plurality of objects; and generating a semantic knowledge graph based on the determined relationships, the semantic knowledge graph including a plurality of query nodes and a plurality of edges, wherein each query node corresponds to a respective object of the plurality of objects, wherein each query node is connected to another query node of the plurality of query nodes by one of the plurality of edges, wherein each edge represents a relationship between the objects connected by the edge.

Abstract: A method, data processing apparatus, and computer code for identifying road intersections includes providing location data obtained from at least one vehicle's trajectory, wherein the location data may include geographical data within a geographical perimeter. The method includes determining node vectors by applying a geographical descriptor model on a target location included in the geographical perimeter. The geographical descriptor model includes a plurality of multiscale node descriptors including a target multiscale descriptor and neighboring multiscale descriptors. Each of the plurality of multiscale node descriptors includes at least two shape descriptors of different geographical resolution. Each of the neighboring locations is at a respective geographical distance from the target location. The node vectors may be respectively determined for each of the plurality of multiscale node descriptors.

Abstract: Information for a knowledge graph is accessed. The knowledge graph has nodes and edges of a network and has information about security incident(s) in the network. Related entities from the knowledge graph are grouped together, where the related entities that are grouped together are determined not only by types of the entities, but also by threat(s) impacting the entities. The threat(s) correspond to the security incident(s). The grouped related entities are arranged in visualization data in order that the visualization data are configured to provide a visualization of the knowledge graph with the grouped related entities. The visualization data are output. Methods, apparatus, and computer program products are disclosed.

Abstract: A system for automated information support processing is disclosed. The system may receive a natural language information support request from a plain text input channel. The system may determine a first user intent based on the information support request. The system may compare the first user intent with a set of support rules. The system may determine a dispositioned outcome based on the set of support rules and the user intent. The system may return the dispositioned outcome.

Abstract: An information processing apparatus includes: a memory; and a processor coupled to the memory and the processor configured to: acquire a plurality of sample videos; identify a position and time at which an attribute appears in each of the plurality of sample videos, the attribute being output by each of one or more pre-trained models to which each of the plurality of sample videos is input; cluster attribute labels based on the position and time of the attribute for each of the plurality of sample videos; and generate a rule by combining attribute labels included in a cluster having a highest frequency of appearance among cluster groups obtained for all of the plurality of sample videos.

Abstract: Aspects of the invention include a computer-implemented method by executing, via a processor, a bottleneck model training process for microservices in a microservice system, wherein for each of the microservices the bottleneck model training process filters out a subset of training data based at least in part on a current situation setting. Building, via the processor, a bottleneck indicator model for each of the microservices using information from the bottleneck model training process, convergence points for an expected response time. Executing, via the processor, a bottleneck identification process for providing system alerts when a bottleneck is identified, wherein the bottleneck identification process uses analysis to monitor a data stream according to the bottleneck indicator model which uses the convergence points for response times.

Abstract: A method for managing pool device resources, the method comprising obtaining, by a resource use manager, a plurality of data points, generating a resource prediction model based on the plurality of data points, and initiating access to a PCI bus device operating on a pool device using a virtual switch operating on a second pool device based on the resource prediction model.

Abstract: A shift-left analysis system receives information regarding an application implemented by one or more microservices. The system determines a microservice performance metric based on a performance prediction model for each microservice of the application. The system outputs an application performance metric for the application based on the microservice performance metrics determined for the one or more microservices of application.

Abstract: A system and method for providing power consumption data for a distributed system in blockchains in a computer environment are presented. Energy consumption of one or more blockchain nodes in a blockchain network that achieves a consensus by a proof of work on a plurality of transactions upon executing a cryptographic operation may be determined.

Abstract: A method predicts custom fields from text. Transaction text is normalized from transaction data to generate normalized text. A field prediction and a type prediction are selected using prediction data and the normalized text. The prediction data is generated using a machine learning model trained to identify field predictions from free form text. The field prediction and the type prediction are presented to a client device. In response to user input from the client device, the transaction data is updated with the field prediction.

Abstract: A system for determining risky events includes an input interface and a processor. The input interface is for receiving sensor data on environmental conditions. The processor is for determining whether the environmental conditions indicate an increase in event probability and, in the event that environmental conditions indicate the increase in the event probability, adjusting an event detection threshold.

Abstract: A method for managing data includes obtaining a request for a machine learning (ML) pipeline selection from a client, wherein the request comprises a training dataset and a domain of the training dataset, and in response to the request: identifying a set of ML pipelines based on the domain, obtaining runtime statistics for the set of ML pipelines using the domain and at least a portion the training dataset, generating, using a user preference model, an ordering of the set of ML pipelines based on the runtime statistics and user preferences, and presenting the ordering, the runtime statistics, and a notification based on the ordering to the client.

Abstract: Systems, apparatuses, methods, and computer program products are disclosed for quantum entanglement authentication (QEA). An example method includes generating, at a first computing device, a first number based on a subset of a first set of entangled quantum particles comprised by a quantum authentication device and associated with the first computing device. Each entangled quantum particle in the first set of entangled quantum particles may be entangled with a respective entangled quantum particle in a second set of entangled quantum particles associated with a second computing device. The example method further includes transmitting an electronic identification of the subset of the first set of entangled quantum particles to the second computing device.

Abstract: Systems, computer-implemented methods, and computer program products to facilitate iterative quantum amplitude estimation are provided. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise an iterative quantum amplitude estimation component that increases a multiplier value of a confidence interval in an estimation problem to a defined value that positions the confidence interval in a defined plane of a defined circle. The computer executable components can further comprise a measurement component that captures a quantum state measurement of a qubit in a quantum circuit based on the defined value.

Abstract: Computational systems and methods employ characteristics of a quantum processor determined or sampled between a start and an end of an annealing evolution per an annealing schedule. The annealing evolution can be reinitialized, reversed or continued after determination. The annealing evolution can be interrupted. The annealing evolution can be ramped immediately prior to or as part of determining the characteristics. The annealing evolution can be paused or not paused immediately prior to ramping. A second representation of a problem can be generated based at least in part on the determined characteristics from an annealing evolution performed on a first representation of the problem. The determined characteristics can be autonomously compared to an expected behavior, and alerts optionally provided and/or the annealing evolution optionally terminated based on the comparison. Iterations of annealing evolutions may be performed until an exit condition occurs.

Abstract: A quantum computer includes an efficient and exact quantum circuit for performing quantum state compression.

Abstract: Controlling an automated agent in an environment includes obtaining high-quality data regarding a current state of the automated agent; identifying a behavior model; determining a trajectory estimate for the automated agent based on the current state of the automated agent and the behavior model; determining a final trajectory for the automated agent using the trajectory estimate; and controlling the automated agent according to the final trajectory.

Abstract: An embodiment may include a machine learning based classifier that maps input observations into respective categories and a database containing a corpus of training data for the classifier. The training data includes a plurality of entries, each entry having an observation respectively associated with a ground truth category thereof. A computing device may be configured to select, from the training data, a plurality of subsets each containing a different number of entries. The computing device may also be configured to, for each particular subset: (i) divide the particular subset into a training portion and a validation portion, (ii) train the classifier with the training portion, (iii) provide the validation portion as input to the classifier as trained, and (iv) based on how entries of the validation portion are mapped to the categories, determine a respective precision for the particular subset.

Abstract: Disclosed herein an artificial intelligence apparatus for updating an artificial intelligence model including a memory configured to store an artificial intelligence model and training data for the artificial intelligence model and a processor configured to receive sensor data, calculate a confidence level of the received sensor data for the stored artificial intelligence model, label the received sensor data if the calculated confidence level is less than a reference value, store the labeled received sensor data in the memory as the training data or test data, and update the stored artificial intelligence model using the stored training data.

Abstract: A system may automatically generate a predictive machine learning model by automatically performing various processes based on an analysis of the data as well as metadata associated with the data. The system may accept a selection of data and a prediction field from the data. The system may automatically generate a set of features based on the data and may automatically remove certain features that cause inaccuracies in the model. The system may balance the data based on a representation rate of certain outcomes. The system may train and select a model based on several candidate models. The system may then perform the predictions based on the selected model and send an indication of the predictions to a user.

Abstract: Mechanisms are provided for implementing a virtual corpus engine that receives an inquiry to be processed and analyzes the inquiry to extract one or more features of the inquiry. The virtual corpus engine selects a weight matrix associated with a virtual corpus based on the extracted one or more features of the inquiry. The virtual corpus comprises a plurality of actual corpora of information. The weight matrix comprises a separate weight value for each actual corpus in the plurality of actual corpora. The virtual corpus engine processes the inquiry using a set of selected actual corpora selected from the plurality of actual corpora based on the weight values in the weight matrix and receives results of the processing of the inquiry using the set of selected actual corpora. The virtual corpus engine outputs the results of the processing of the inquiry.

Abstract: A computer-implemented method according to one embodiment includes receiving a single instance of training data, simplifying the single instance of training data to create a single instance of simplified training data, generating a plurality of training data variants, based on the single instance of simplified training data, and training a machine learning model, utilizing the plurality of training data variants.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training machine learning systems. One of the methods includes receiving a plurality of training examples; and training a machine learning system on each of the plurality of training examples to determine trained values for weights of a machine learning model, wherein training the machine learning system comprises: assigning an initial value for a regularization penalty for a particular weight for a particular feature; and adjusting the initial value for the regularization penalty for the particular weight for the particular feature during the training of the machine learning system.

Abstract: Described herein are systems and techniques for privacy-preserving unsupervised learning. The disclosed system and methods can enable separate computers, operated by separate entities, to perform unsupervised learning jointly based on a pool of their respective data, while preserving privacy. The system improves efficiency and scalability, while preserving privacy and avoids leaking a cluster identification. The system can jointly compute a secure distance via privacy-preserving multiplication of respective data values x and y from the computers based on a 1-out-of-N oblivious transfer (OT). In various embodiments, N may be 2, 4, or some other number of shares. A first computer can express its data value x in base-N. A second computer can form an ×N matrix comprising random numbers mi,0 and the remaining elements mi,j=(yjNi-mi,0) mod . The first computer can receive an output vector from the OT, having components mi=(yxi Ni-mi,0) mod .

Abstract: A method of training a reinforcement machine learning computer system. The method comprises providing a machine-learning computer programming language including a pre-defined plurality of reinforcement machine learning criterion statements, and receiving a training specification authored in the machine-learning computer programming language. The training specification defines a plurality of training sub-goals with a corresponding plurality of the reinforcement machine learning criterion statements supported by the machine-learning computer programming language. The method further comprises computer translating the plurality of training sub-goals from the training specification into a shaped reward function configured to score a reinforcement machine learning model configuration with regard to the plurality of training sub-goals.

Abstract: The present disclosure relates to systems and methods for a machine learning platform that generates a library of components to generate machine learning models and machine learning applications. The machine learning infrastructure system allows a user (i.e., a data scientist) to generate machine learning applications without having detailed knowledge of the cloud-based network infrastructure or knowledge of how to generate code for building the model. The machine learning platform can analyze the identified data and the user provided desired prediction and performance characteristics to select one or more library components and associated API to generate a machine learning application. The machine learning can monitor and evaluate the outputs of the machine learning model to allow for feedbacks and adjustments to the model. The machine learning application can be trained, tested, and compiled for export as stand-alone executable code.

Abstract: The disclosed systems and methods are directed to a computer-implemented method for use in designing a service. In at least one embodiment, a method includes designing a service with one or more generic virtual network functions (VNFs), where the generic VNFs are defined independent of vendor sourcing information. One or more trained machine learning (ML) models are used to identify VNFs available from VNF vendors that may source one or more VNFs similar to the generic VNFs. The service is implemented using VNFs provided by one or more VNF vendors, where the VNFs provided by the one or more VNF vendors have network functionality generating similar to the one or generic VNFs.

Abstract: A system and method for augmenting unlabeled imaging data of invasive medical devices is disclosed. An imaging device can generate images of the invasive medical device inside a patient. A trained model for the invasive medical device can be trained on annotated images of the invasive medical device having labels indicating orientation and position information. An imaging computer system can apply the trained model to unlabeled images of the invasive medical device within the patient to determine a current orientation and a current position of the invasive medical device inside the patient. The images of the invasive medical device, visual orientation information representing the current orientation of the invasive medical device, and visual position information representing the current position of the invasive medical device inside the patient can be outputted to a display.

Abstract: A document processing system and method for performing document classification by machine learning include an input module, a processing module, and at least one storage module preconfigured with a classification folder matching a code. Upon completion of a first-instance model construction procedure, the input module receives a document image. The processing module compares the document image with a machine learning model information to generate a computation result and stores the document image in the classification folder according to the computation result. Therefore, classification of the document images is automated according to the code of the corresponding classification folder, thereby enhancing the accuracy and efficiency of document classification.

Abstract: Techniques for determining embedding drift score in a machine learning model. The techniques can include: obtaining one or more first embedding vectors based on at least one first prediction of a machine learning model; filtering the first embedding vectors based on a slice of the first prediction; determining a first average vector by averaging each dimension of the filtered first embedding vectors; obtaining one or more second embedding vectors on at least one second prediction of the machine learning model; filtering the second embedding vectors based on a slice of the second prediction; generating a second average vector by averaging each dimension of the filtered second embedding vectors; and determining an embedding drift score based on a distance measure of the first average vector and the second average vector.

Abstract: A method including training predictor machine learning models (MLMs) using a first data set. The trained predictor MLMs are trained to predict classifications of data items in the first data set. The method also includes training confidence MLMs using second classifications, output by the trained predictor MLMs. The method also includes generating an aggregated ranked list of classes based on third classifications output by the trained predictor MLMs and second confidences output by the trained confidence MLMs. The method also includes training an ensemble confidence MLM using the aggregated ranked list of classes to generate a trained ensemble confidence MLM. The trained ensemble confidence MLM is trained to predict a corresponding selected classification for each corresponding data item in a training data set containing second data items similar to the first data items.

Abstract: A system and method for automatically calculating an accurate recommended dosage for hormone replacement therapy and automating the life cycle of a patient's treatment over time. The system and method can automatically acquire relevant patient parameters and apply a consistent formulaic approach to help reduce incorrect dosage determinations. A pellet insertion size may be determined and documented based on a calculated dosage, and an insertion side and lot numbers may be tracked and managed. In addition, corresponding revenues may be tracked and profitability may be reported for hormone replacement therapy practices.

Abstract: A computer-implemented method for managing services is disclosed, which comprises providing to a service provider a notification of a service reservation by one or more customer entities, continuously estimating a time of arrival based on a current geolocation of the one or more customer entities, providing to the service provider an indication of the estimated time of arrival, notifying the service provider of an arrival of the one or more customer entities at the location of the service provider based on the current geolocation and the location of the service provider, and responsive to the arrival at the location of the service provider, automatically coupling the one or more customer entities to a communication network of the service provider to confirm the service reservation.

Abstract: Parking space selection and navigation systems and methods are disclosed. One or more computing devices are configured to receive a search request from a first user device to secure a parking space for a vehicle, which includes one or more listing parameter, secure a first parking space stored in a database based on the search request, and determine a first listing value for the first parking space. The computing device(s) are configured to receive information associated with a second parking space and determine a second listing value for the second parking space. The computing device(s) are configured to determine that the second listing value is greater than the first listing value by a predefined value, transmit a message to the first and second user devices, and automatically transmit information relating to the parking reservation to a navigation application to update a destination associated with the second parking space.

Abstract: A vehicle management method includes calculating, based on a desired condition of each user sharing a ride on a vehicle, a first route for the vehicle to travel and a first required time for the vehicle to arrive at a destination on the first route, when a delay time with respect to the first required time exceeds a predetermined time while the vehicle is traveling along the first route, setting an alternative boarding location for a boarding location or an alternative deboarding location for a deboarding location based on the desired condition of each user, calculating a second route including the alternative boarding location or the alternative deboarding location and a second required time for the vehicle to arrive at a destination on the second route, and notifying the users of at least the alternative boarding location or the alternative deboarding location and the second required time.

Abstract: There is a need for more effective and efficient data modeling and/or data visualization solutions. This need can be addressed by, for example, solutions for performing data modeling and/or data visualization in an effective and efficient manner. In one example, solutions for generating a data model with dynamic relational awareness are disclosed. In another example, solutions for processing data retrieval queries using data models with dynamic relational awareness are disclosed. In yet another example, solutions for generating data visualizations using data models with dynamic relational awareness are disclosed. In a further example, solutions for integrating external data objects into data models with dynamic relational awareness are disclosed.

Abstract: A task management platform generates an interactive display tasks based on multi-team activity data of different geographic locations across a plurality of distributed guided user interfaces (GUIs). Additionally the task management platform uses a distributed machine-learning based system to determine a suggested task item for a remote team based on multi-team activity data of different geographic locations.

Abstract: An example method includes receiving, by one or more processors, a representation of an utterance spoken at a computing device; identifying, by a first computational agent from a plurality of computational agents and based on the utterance, a multi-element task to be performed, wherein the plurality of computational agents includes one or more first party computational agents and a plurality of third-party computational agents; and performing, by the first computational agent, a first sub-set of elements of the multi-element task, wherein performing the first sub-set of elements comprises selecting a second computational agent from the plurality of computational agents to perform a second sub-set of elements of the multi-element task.

Abstract: Techniques for scoring data items using a machine-learned model are provided. In one technique, multiple skills are identifying based on a job posting. Multiple attribute values of the job posting are identified. For each identified skill, multiple probabilities are identified, each probability corresponding to a different attribute value of the identified attribute values. The probabilities are input into a machine-learned model to generate multiple scores. Multiple skills of a candidate user are identified. An affinity score between the job posting and the candidate user is generated based the scores and the skills of the candidate user.

Abstract: Computer-implemented systems and methods generate dynamic and interactive user interfaces. In one embodiment, a computerized system includes a user interface and at least one processor configured to receive data related to at least a first request of a department, receive data related to positioning of a plurality of employees of the department, and data indicative of equipment in the possession of the plurality of employees, generate an assignment for at least one employee based on the received data, provide for display at the at least one user interface, an interactive graphical user interface indicative of the assignment to the at least one employee, the graphical user interface including elements for indicating a status of the assignment. The at least one processor monitors whether the assignment has been accepted, and determines whether the assignment has been completed, based in part on interaction with the graphical user interface.

Abstract: The present application includes a method and system for real-time scheduling reallocation. The system receives a schedule which allocates customer service representatives (CSRs) to channels and queues based on analytics rules as well as the forecast work patterns and information on CSR skills and shift patterns. The system then monitors adherence to the schedule by comparing the difference between forecast and current volume and type of work to a reallocation threshold at predetermined intervals. If the difference extends beyond the reallocation threshold, the system reallocates CSRs to update the schedule.

Abstract: A vehicle ride-sharing assist system includes a terminal including a user interface and a terminal processing unit; an action information managing server; and a ride-sharing managing server configured to create an operation schedule based on a desired ride-sharing condition, which includes at least one of a departure place, a departure time, an arrival place, and an arrival time of each user, transmitted from the terminal processing unit. The ride-sharing managing server is configured to set a recommended ride-sharing condition, which includes at least one of the departure place, the departure time, the arrival place, and the arrival time suitable for each user, based on the action information and transmit the recommended ride-sharing condition to the terminal processing unit. The terminal processing unit is configured to cause the user interface to display the recommended ride-sharing condition on receiving the recommended ride-sharing condition from the ride-sharing managing server.

Abstract: A content collaboration system displays a UI allowing a project owner to create and edit a collaboration project, and share the collaboration project with other users. To share the collaboration project, the project owner starts a sharing workflow by providing a user input to the collaboration system UI. The sharing workflow allows the project owner to identify other users with which to share the collaboration project. As part of the sharing workflow, the project owner can input a request via the collaboration system UI to manage (e.g., create or edit) a group of users. In response to such a request, the collaboration system UI causes the group to be created or changed as requested. In response to a request to share the collaboration project with the group the collaboration system UI causes an invitation to access the collaboration project to be sent to the members of the group.

Abstract: A building energy system includes equipment operable to consume, store, or generate one or more resources, a utility connection configured to obtain, from a utility provider, a first resource subject to a load-following-block rate structure and provide the first resource to the equipment, and a controller. The controller provides an objective function indicating a total value of purchasing the first resource from the utility provider over a plurality of time steps. The objective function includes a first term representing a load-following-block of the first resource from the utility provider as being sourced from a first supplier at a fixed rate and a second term representing a remainder of the first resource from the utility provider as being sourced from a second supplier at a variable rate. The controller controls the equipment based on a result of an optimization of the objective function.

Abstract: A system and method for contextually organizing and visualizing electronic work product is provided. The system generally comprises a processor, a power supply, a computing device operably connected to the processor, and a non-transitory computer-readable medium coupled to the processor and having instructions stored thereon. The processor may query the non-transitory computer-readable medium and/or a database for electronic work product based on search parameters input by a professional. The processor may then contextually visualize the electronic work product within the user interface as a professional task. The professional may navigate the professional task via the user interface and create new work product, which will be saved by the system for future task generation. Varying permission levels may allow certain professionals to monitor professional task progress, create new tasks, and assign new tasks as necessary.

Abstract: Workflow sharing system and method are disclosed that allow a workflow server to provide a shared workflow, wherein the shared workflow generates at least one manifest, wherein the at least one manifest comprises a third-party interaction field, a credentials field, an input parameter field, and an output parameter field, wherein the workflow server receives a request for access to the shared workflow and transmits the at least one manifest of the shared workflow in response to the request for access to the shared workflow, wherein the at least one manifest comprises an acceptance requirement of the third-party interaction field, the credentials field, the input parameter field, and the output parameter field, wherein the workflow server receive consents of the acceptance requirement of the at least one manifest; and allows access to the shared workflow in response to the received consent of the acceptance requirement of the at least one manifest.

Abstract: A method of early warning and risk assessment of incidents in a multi-tenant cloud environment is provided. The method includes: capturing a plurality of data metrics; automatically generating derived features from the plurality of captured data metrics; automatically selecting risk assessment features from the derived features and the captured data metrics; and predicting the risk of an incident in the multi-tenant cloud environment within a specified time window in the future and one or more possible root causes of the incident by applying the newly selected risk assessment features to a trained risk assessment model. The trained risk assessment model has been trained using machine learning techniques to predict the risk of an incident in the multi-tenant cloud environment within a specified time window in the future, provide an explanation of possible root causes of the incident, and assign a strength level to each possible root cause.