Abstract: Methods and systems for collecting and analyzing sensor data to predict water fixture failure and water consumption are provided. In one embodiment, a method is provided that includes receiving sensor data regarding a water fixture. Changepoints may then be calculated within the sensor data and the sensor data may be split into intervals at the changepoints. A machine learning model may then be used to classify the intervals and a status of the water fixture and water consumption may be identified based on the classified intervals.

Abstract: Systems and methods are described to combine two or more linear models into a combined linear model. Two or more linear models and an observation of interest are selected. The linear models are concurrent with respect to the observation of interest. The observation of interest includes a class value and a feature vector. A distance is selected, and a plurality of feature vectors are selected that are within the distance of the feature vector associated with the observation of interest. These feature vectors are input to the selected linear models and a plurality of class values are generated. These class values and the selected feature vectors are used to generate a combined linear model. The combined model is either a mean local linear surrogate model or a linearly weighted local surrogate model.

Abstract: Provided is a process including: obtaining, for a plurality of entities, entity logs, wherein: the entity logs comprise events involving the entities, a first subset of the events are actions by the entities, at least some of the actions by the entities are targeted actions, and the events are labeled according to an ontology of events having a plurality of event types; training, with one or more processors, based on the entity logs, a predictive machine learning model to predict whether an entity characterized by a set of inputs to the model will engage in a targeted action in a given duration of time in the future; and storing the trained predictive machine learning model in memory.

Abstract: A device comprises determining, if an instruction to open a first drawing file is accepted, whether the first drawing file and a second drawing file associated with the first drawing file have been edited at the same time; determining whether the second drawing file has been edited after previous editing of the first drawing file; determining, if the first drawing file and the second drawing file have been edited at the same time or if they have not been edited at the same time but the second drawing file has been edited after the previous editing of the first drawing file, whether there is a difference between attributes of identical drawing elements included in the respective drawing files; and executing, if there is a difference between the attributes, attribute change processing on the first drawing file.

Abstract: A cognitive information processing system environment comprising a plurality of data sources; a cognitive inference and learning system coupled to receive data from the plurality of data sources, the cognitive inference and learning system processing the data from the plurality of data sources to perform a cognitive learning operation, the cognitive learning operation applying a cognitive learning technique to generate a cognitive learning result; and, a destination, the destination being updated based upon the learning result.

Abstract: Substrates to be processed are partitioned based on pre-processing data that is associated with substrates before a process step. The data is partitioned using a partition rule and the substrates are partitioned into subsets in accordance with subsets of the data obtained by the partitioning. Corrections are applied, specific to each subset. The partition rule is obtained using decision tree analysis on a training set of substrates. The decision tree analysis uses pre-processing data associated with the training substrates before they were processed, and post-processing data associated with the training substrates after being subject to the process step. The partition rule that defines the decision tree is selected from a plurality of partition rules based on a characteristic of subsets of the post-processing data. The associated corrections are obtained implicitly at the same time.

Abstract: Embodiments of the invention are directed to accurate resource interaction opportunity decisioning. Contextual information is extracted, in real-time or otherwise, which takes into account the circumstances surrounding the presentation of resource interaction opportunities to the potential user. A decisioning model from amongst a plurality of models is determined based on implementation of an explainability calculator that assesses the accuracy, risk and complexity of the models in relation to the resource interaction opportunity decisions rendered by the models. A relationship builder is implemented that is configured to build a relationship network amongst potential users and resource interaction opportunity providers and subsequently use the indirect relationships provided by the network as a basis for determining which resource interaction opportunity providers should provide resource interaction opportunities to the potential users.

Abstract: A reasoning engine is disclosed. Contemplated reasoning engines acquire data relating to one or more aspects of various environments. Inference engines within the reasoning engines review the acquire data, historical or current, to generate one or more hypotheses about how the aspects of the environments might be correlated, if at all. The reasoning engine can attempt to validate the hypotheses through controlling acquisition of the environment data.

Abstract: A behavioral analytics platform may obtain a first data set associated with a first user and a second data set associated with a second user. The behavioral analytics platform may determine a first set of behavioral categories to classify the historical user activity data associated with the first user based on a first set of behavior vectors and determine a second set of behavioral categories to classify the historical user activity data associated with the second user based on a second set of behavior vectors. The behavioral analytics platform may populate one or more user interfaces that are accessible to the first user and/or the second user based on one or more values representing a degree to which the first set of behavioral categories and the second set of behavioral categories correspond to complementary behavioral tendencies, which may be determined using a machine learning technique.

Abstract: The invention relates to a computer-implemented method for managing multiple services in a DBMS. The services respectively are configured to process data that is stored in or is received for being stored in a database managed by the DBMS. The method comprises providing a service manager operatively coupled to the DBMS; registering the multiple services automatically and dynamically at runtime of the DBMS at the service manager; and automatically managing, by the service manager, dependencies between different ones of the multiple registered services.

Abstract: A system for image classification is disclosed that includes a central system configured to provide high reliability image data processing and recognition and a plurality of endpoint systems, each configured to provide image data processing and recognition with a lower reliability than the central system and to generate probability data. A decision switch disposed at each of the plurality of endpoint systems is configured to receive the probability data and to determine whether to deny access, grant access or generate a referral message to the central system, wherein the referral message includes at least a set of image data generated at the endpoint system.

Abstract: Under certain conditions, a fermion in a superconductor can separate in space into two parts known as Majorana zero modes, which are immune to decoherence from local noise sources and are attractive building blocks for quantum computers. Here we disclose a metal-based heterostructure platform to produce these Majorana zero modes which utilizes the surface states of certain metals in combination with a ferromagnetic insulator and a superconductor. This platform has the advantage of having a robust energy scale and the possibility of realizing complex circuit designs using lithographic methods. The Majorana zero modes are interrogated using planar tunnel junctions and electrostatic gates to selectively tunnel into designated pairs of Majorana zero modes. We give example of qubit designs and circuits that are particularly suitable for the metal-based heterostructures.

Abstract: A second problem Hamiltonian may replace a first problem Hamiltonian during evolution of an analog processor (e.g., quantum processor) during a first iteration in solving a first problem. This may be repeated during a second, or further successive iterations on the first problem, following re-initialization of the analog processor. An analog processor may evolve under a first non-monotonic evolution schedule during a first iteration, and second non-monotonic evolution schedule under second, or additional non-monotonic evolution schedule under even further iterations. A first graph and second graph may each be processed to extract final states versus a plurality of evolution schedules, and a determination made as to whether the first graph is isomorphic with respect to the second graph. An analog processor may evolve by decreasing a temperature of, and a set of quantum fluctuations, within the analog processor until the analog processor reaches a state preferred by a problem Hamiltonian.

Abstract: A system for applying orbital angular momentum (OAM) to electrons of a semiconductor material comprises a light source generator for generating a plane wave light beam. Orbital angular momentum (OAM) processing circuitry applies at least one orbital angular momentum to the plan wave light beam to generate an OAM light beam. The OAM processing circuitry controls transitions of electrons between quantized states within the semiconductor material to perform quantum entanglement within the semiconductor material responsive to the at least one orbital angular momentum applied to the plane wave light beam. A transmitter transmits the OAM light beam at the semiconductor material to induce the transitions of the electrons between the quantize states and perform the quantum entanglement within the semiconductor material.

Abstract: Methods, systems, and computer-readable storage media for receiving a set of documents matched by a ML model, each document in the set of documents including a computer-readable electronic document, processing a set of pairwise features, the ML model, and the set of documents by an explanation framework to generate a set of raw explanations, the set of raw explanations including one or more raw explanations, each raw explanation including a pairwise feature and an importance score, for each raw explanation, identifying a natural language template based on the pairwise feature and the importance score, and populating the natural language template with one or more parameters provided from the set of documents to provide a natural language explanation for matching of the documents in the set of documents by the ML model.

Abstract: Disclosed herein are systems and methods for generating adverse-action reasons for adverse credit-application determinations. In an embodiment, a server calculates a log-odds contribution for each of multiple input variables of a machine-learning model for each of multiple training-data observations. The server identifies a working minimum log-odds contribution and from that a maximum score for each input variable. The server calculates a log-odds contribution and from that an actual score for each input variable for an application data set. The server calculates a score difference between the maximum and actual scores for each input variable. The server outputs an adverse-action report that is associated with the application data set. The adverse-action report includes an indication for each of a predefined number of the input variables having the highest score differences.

Abstract: Parallel training of a machine learning model on a computerized system may be provided. Computing tasks can be assigned to multiple workers of a system. A method may include accessing training data. A parallel training of the machine learning model can be started based on the accessed training data, so as for the training to be distributed through a first number K of workers, K>1. Responsive to detecting a change in a temporal evolution of a quantity indicative of a convergence rate of the parallel training (e.g., where said change reflects a deterioration of the convergence rate), the parallel training of the machine learning model is scaled-in, so as for the parallel training to be subsequently distributed through a second number K? of workers, where K>K??1. Related computerized systems and computer program products may be provided.

Abstract: Examples of the present disclosure describe systems and methods for visit prediction using machine learning (ML) attribution techniques. In aspects, data relating to users and their venue visits is collected and merged with data relating to various directed information impressions. Features of the merged data are identified for one or more time intervals and assigned values and/or labels. The identified features and corresponding values/labels may be used to train an ML model to provide a visit probability for each user represented in the merged data.

Abstract: Methods, systems, and computer-readable storage media for determining, by an automated regression detection system (ARDS), that training of a ML model is complete, the ML model being a version of a previously trained ML model, and in response, automatically, by the ARDS: retrieving the ML model, executing regression testing and detection using the ML model, generating regression results relative to the previously trained ML model, and publishing the regression results.

Abstract: A parser is deployed early in a machine learning pipeline to read raw data and collect useful statistics about the raw data's content to determine which items of raw data exhibit a proxy for feature importance for the machine learning model. The parser operates at high speeds that approach the disk's absolute throughput while utilizing a small memory footprint. Utilization of the parser enables the machine learning pipeline to receive a fraction of the total raw data that would otherwise be available. Several scans through the data are performed, by which proxies for feature importance are indicated and irrelevant features may be discarded and thereby not forwarded to the machine learning pipeline. This reduces the amount of memory and other hardware resources used at the server and also expedites the machine learning process.

Abstract: An apparatus is described for training a behavior of an agent in a physical or digital environment. The apparatus comprises a memory storing the location of at least one reward token in the environment. The location has been specified by a user. At least one processor executes the agent in the environment according to a behavior policy. The processor is configured to observe values of variables comprising: an observation of the agent, an action of the agent and any reward resulting from the reward token. The processor is configured to update the behavior policy using reinforcement learning according to the observed values.

Abstract: A method for determining an operating state of a machine includes measuring a signal of the machine, applying the measured signal to a machine-learned classifier or machine learning model learned on machine signals and associated operating states, generating the operating state of the machine based on the application of the measured signal to the machine-learned classifier or machine learning model, and outputting the operating state of the machine.

Abstract: A computer-implemented method of machine-learning is described that includes obtaining a test dataset of scenes. The test dataset belongs to a test domain. The method includes obtaining a domain-adaptive neural network. The domain-adaptive neural network is a machine-learned neural network taught using data obtained from a training domain. The domain-adaptive neural network is configured for inference of spatially reconfigurable objects in a scene of the test domain. The method further includes determining an intermediary domain. The intermediary domain is closer to the training domain than the test domain in terms of data distributions. The method further includes inferring, by applying the domain-adaptive neural network, a spatially reconfigurable object from a scene of the test domain transferred on the intermediary domain. Such a method constitutes an improved method of machine learning with a dataset of scenes comprising spatially reconfigurable objects.

Abstract: Provided is a process including: obtaining, for a plurality of entities, entity logs, wherein: the entity logs comprise events involving the entities, a first subset of the events are actions by the entities, at least some of the actions by the entities are targeted actions, and the events are labeled according to an ontology of events having a plurality of event types; training, with one or more processors, based on the entity logs, a predictive machine learning model to predict whether an entity characterized by a set of inputs to the model will engage in a targeted action in a given geographic locale in the future; and storing the training the trained predictive machine learning model in memory.

Abstract: Respective statistical distributions of a target variable within a proposed training data set and a proposed test data set for a machine learning model are obtained. A metric indicative of the difference between the two statistical distributions is computed. The difference metric is used to determine whether the proposed test data set is acceptable to evaluate the machine learning model.

Abstract: A machine-learning based artificial intelligence device for finding an estimate of patent quality, such as patent lifetime or term is disclosed. Such a device may receive a first set of patent data and generate a list of binary classifiers. A candidate set of binary classifiers may be selected and using a heuristic search, for example an artificial neural network (ANN), a genetic algorithm, a final set of binary classifiers is found by maximizing iteratively a yield according to a cost function, such an area under a curve (AUC) of a receiver operating characteristic (ROC). The device may then receive patent information for a target patent and report an estimate of patent quality according to the final set of binary classifiers.

Abstract: A system coordinates services between users and providers. The system trains a computer model to predict a user state of a user using data about past services. The prediction is based on data associated with a request submitted by a user. Request data can include current data about the user's behavior and information about the service that is independent of the particular user behavior or characteristics. The user behavior may be compared against the user's prior behavior to determine differences in the user behavior for this request and normal behavior of prior requests. The system can alter the parameters of a service based on the prediction about the state of the user requesting the service.

Abstract: Testing machine learning sensors by adding obfuscated training data to test data, and performing real time model fit analysis on live network traffic to determine whether to retrain.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for debugging correctness issues in training machine learning models. In one aspect, a method comprises training a first machine learning model using a first computing system having a first configuration; training a second machine learning model using a second computing system having a second configuration, wherein the second configuration of the second computing system is different than the first configuration of the first computing system; and determining, for each of a plurality of shared training operations that are performed by both the first computing system and the second computing system, a respective similarity measure that measures a similarity between: a first training output generated by the first computing system by performing the shared training operation, and a second training output generated by the second computing system by performing the shared training operation.

Abstract: In a management system for managing vehicle parking, a communication unit is configured to receive, from a user, a vehicle allocation request including a number of occupants, a desired boarding location, and a desired boarding time. A vehicle selection unit is configured to secure one or more vehicles corresponding to the number of occupants based on the vehicle allocation request. A reservation control unit is configured to, in response to a parking space for the secured vehicle being allocated to a parking candidate site corresponding to the desired boarding location during a parking time period corresponding to the desired boarding time, allocate a reservation for the parking space during the parking time period to the parking candidate site and allocating the vehicle to the user.

Abstract: Access control to a digital event is disclosed. An access request to access a digital event is received from a first device. The access request may include an event identifier and a password. The event identifier may comprise a unique uniform resource locator associated with an electronic ticket for the digital event. It is determined that a second device is currently accessing the digital event, wherein the second device was previously authorized to access the digital event based on the unique uniform resource locator associated with the electronic ticket for the digital event. The first device is caused to receive a digital media stream associated with the digital event and the second device is caused to stop receiving the digital media stream associated with the digital event.

Abstract: A method of managing events is disclosed. It is determined that a user is present at a particular location within an event venue. It is determined that the user has reserved the event venue for a particular time period. A selection is received from the user of a particular merchant that is committed to providing services at the event during the particular time period. The particular location within the event is associated with the particular merchant. A notification of the association of the particular location within the event venue with the particular merchant is generated for communication to the particular merchant.

Abstract: Itineraries may be recommended using rider context information relating to a rider. The rider context information may be obtained from various sources. Location information including an origin and a destination may obtained. A set of itineraries may be generated based on the rider context information. Each itinerary may include at least one mode of transportation to allow the rider to travel from the origin to the destination. Each itinerary in the set of itineraries may be ranked based on the rider context information.

Abstract: A table management system is described. The table management system includes a host display device for displaying table placements within a room. The host display device includes more than one staff identifier device coupled to staff personnel and more than one table sensor including a communication device. The table management system also includes a server running a table management system algorithm and an aggregator device in communication with the server and in communication with the more than one table sensor. The aggregator device receives presence information and staff information from the more than one table sensor and communicating that information to the server. The server runs a software algorithm for managing table assignments and communicates table assignment information to the host display device.

Abstract: A computing system can assign a transport request to a high capacity vehicle (HCV) corridor of a plurality of HCV corridors, where the HCV corridor is associated with a plurality of possible rendezvous locations and a plurality of possible routes that can be traveled by individual HCVs. The computing system can determine, from the transport request of the requesting user, an optimal pick-up location from the plurality of possible rendezvous locations of the HCV corridor for an HCV to rendezvous with the requesting user.

Abstract: The disclosed computer-implemented method may include (i) receiving a first transport request and a second transport request, (ii) evaluating a fitness of matching the first and second transport requests to be fulfilled by a transport provider, based at least partly on a transportation overlap between the first and second transport requests, (iii) generating a simulated future transport request, (iv) evaluating a fitness of matching the first transport request with the simulated future transport request, based at least in part on a transportation overlap between the first transport request and the simulated future transport request, and (v) matching the first and second transport requests based at least in part on the fitness of matching the first and second transport requests and based at least in part on the fitness of matching the first transport request with the simulated future transport request. Various other methods, systems, and computer-readable media are disclosed.

Abstract: A system for tracking, assigning, and issuing equipment to security agency personnel (e.g., officers, administration, command, armorer). An armorer is assigned the task of tracking, assigning, and issuing equipment. Equipment may be assigned to a particular individual officer. The equipment provided to and used by officers may capture (e.g. gather, produce) information (e.g. audiovisual information, data logs, recordings). Equipment may provide information to the armorer via near field communication (NFC) circuits. The information captured may be used alone or in combination with other information received and stored by an evidence management system. The information captured by equipment assigned to a particular individual may be associated with that individual. An armorer may use information supplied by equipment, evidence management system, officers, or other sources to assist in the assignment process.

Abstract: A petroleum oilfield enterprise operations management system for collecting, presenting and evaluating raw and processed data and information related to the extraction and distribution of hydrocarbon products. The system allows for multiple sources of information to the collected, parsed, manipulated and presented to those making decisions related a process or business.

Abstract: A method for generating an industry chain graph is provided. The method may include acquiring a key word related to an industry field, and retrieving industry data corresponding to the industry field based on the key word related to the industry field from one or more information sources. The method may also include determining a plurality of components in an industry chain of the industry field based on the one or more information sources, and constructing one or more links between the plurality of components in the industry chain based on the industry data. The method may further include generating the industry chain graph related to the industry field based on the plurality of components and the one or more links.

Abstract: A method of displaying survey results is disclosed. In one embodiment, for each response to each item by each survey respondent, a calibrated score is determined. The calibrated score is based on a probability that a person having the collection of respondent segments associated with the survey respondent would provide either the response to the item or any of the response options to the item that are lower on the ordinal scale, or the response to the item or any of the response options to the item that are higher on the ordinal scale. An organization composite score is determined based on the calibrated score for each response. A display includes images corresponding with the organization composite score and other organization composite scores. Each image is located with respect to a first axis based on the associated organization composite score.

Abstract: In some examples, extended reality based immersive project workspace creation may include obtaining sensing data from at least one sensor of a device worn by a user, and analyzing, based on the sensing data, an environment of the user. A plurality of insights associated with the user may be determined, and prioritized based on a plurality of prioritization criteria. An insight of the plurality of prioritized insights associated with the user may be rendered in the environment of the user. Further, the rendering of the insight may be controlled based on an interaction of the user with the rendered insight.

Abstract: A system for designing and executing control loops in a cloud environment includes a control platform implemented in the cloud environment having a data collection, analytics and events module, a policy module and an application controller module. The system includes a business process management application coupled to the control platform having a control loop designer module for designing a control loop template and a workflow engine for distributing the control loop template. The business process management application is coupled to the data collection analytics and events module of the control platform as well as the policy module in the application controller module to control platform. The control loop is activated by the control platform.

Abstract: Described herein are improved systems and methods for overcoming technical problems associated with the use of Monte Carlo simulation methods, such as problems associated with applications of Monte Carlo simulation methods that are searching for more definite answers. In some embodiments described herein, improved systems and methods overcome the technical problem of the results of Monte Carlo simulations providing approximations and/or non-optimal results (or at least non-enhanced results). Thus, such embodiments can provide more enhanced answers to limiting risks; and in some cases, such embodiments can even provide optimal answers to limiting risks. In some embodiments, machine learning can be used to provide more enhanced answers to limiting risks; and in some cases, such embodiments can use machine learning to provide optimal answers to limiting risks discovered through Monte Carlo simulations.

Abstract: Provided are systems and methods for evaluating risks associated with a manufacturing process including a network interface configured to receive a request to evaluate a purchase order associated with a deliverable that includes one or more components for manufacture, a processor configured to evaluate a set of metrics associated with the deliverable to identify a status of each of the set of metrics, and an output configured to generate a risk evaluation representing a risk of delivery of the deliverable.

Abstract: A method of assessing a risk level of an enterprise using cloud-based services from one or more cloud service providers includes assessing provider risk scores associated with the one or more cloud service providers; assessing cloud service usage behavior and pattern of the enterprise; and generating a risk score for the enterprise based on the provider risk scores and on the cloud service usage behavior and pattern of the enterprise. The risk score is indicative of the risk of the enterprise relating to the use of the cloud-based services from the one or more cloud service providers.

Abstract: Embodiments are directed to managing documents over a network. A machine learning (ML) engine analyzes a plurality of documents associated with actions that were performed previously. The ML engine determines critical events associated with the performance of the actions based on the plurality documents. The ML engine generates ML models based on the critical events to compute risk values that may be associated with the critical events. In response to a request to compute risk values associated with pending actions, the ML engine determines documents that are associated with the pending actions based on the request. The ML engine determines the critical events associated with pending actions based on the documents. The ML engine employs the ML models to generate the risk values based on the documents and the critical events. The ML engine provides the risk values in response to the request.

Abstract: A risk information processing method is used in a risk information processing system that manages a degree of risk at a target spot at which a moving object is located. The method includes: receiving sensor data collected at each of two spots different from the target spot; determining a degree of risk of an event at each of the two spots from the sensor data collected at each of the two spots; storing, as risk information the determined degree of risk associated with each of the two spots; estimating first risk information associated with the target spot without sensor data of first event at the target spot, by using the stored risk information, the first risk information being a degree of risk of the first event at the target spot; and outputting information for the target spot based on the estimated first risk information.

Abstract: Various embodiments are directed to techniques for defining and optimizing the boundaries of geospatial areas predictive of various outcomes. A geographic area of interest is defined, and a model trained to predict the variable of interest within the geographic area of interest is trained using training data selected for the geographic area. The model is scored for each cell in a meshed grid defined over the geographic area of interest and, thereafter, a contour-finding algorithm is applied to the grid to define the optimized geographic area.

Abstract: Systems and computer-implemented methods provided herein determine a metric for brand vitality (VIT) for social media content. VIT is a unique score for each post of social media content that acts as a proxy for impact on consumers. By avoiding the need to conduct costly consumer research for each brand or marketing campaign on social media, the determination of VIT provides a cost-effective and automated yet still meaningful assessment of the reach and exposure of the brand or marketing campaign. Importantly, the determination of VIT is scalable in a way that polling and consumer research is not.

Abstract: A personnel evaluation method and system is provided which require a few steps for personnel evaluation and objectively evaluate personnel. In such a system, an evaluator simply inputs or selects in the evaluation item column whether a subject (an evaluatee) is rated higher or lower than the evaluator oneself as a reference, and thus a only a few steps are required for evaluation. And then it is possible to determine whether the subject has a high rating or a low rating by calculating a mean rating of the subject with obtaining such ratings from a plurality of evaluators.