Abstract: Described herein are embodiments of systems, method, and computer programs for estimating affective response to a token instance of interest utilizing a predicted affective response to a background token instance. The affective response is computed based on the difference between a measured affective response of the user and the predicted affective response. Where the measured affective response is a measurement of the affective response of the user to a simultaneous exposure to both the background token instance and the token instance of interest.

Abstract: Systems and techniques for predictive data analytics are described. In a method for selecting a predictive model for a prediction problem, the suitabilities of predictive modeling procedures for the prediction problem may be determined based on characteristics of the prediction problem and/or on attributes of the respective modeling procedures. A subset of the predictive modeling procedures may be selected based on the determined suitabilities of the selected modeling procedures for the prediction problem. A resource allocation schedule allocating computational resources for execution of the selected modeling procedures may be generated, based on the determined suitabilities of the selected modeling procedures for the prediction problem. Results of the execution of the selected modeling procedures in accordance with the resource allocation schedule may be obtained. A predictive model for the prediction problem may be selected based on those results.

Abstract: A computer-implemented method, including receiving by one or more computer systems input information that represents a multi-dimensional communication; detecting, based on contents of the input information, a plurality of communication inputs; applying one or more weighted values to one or more of the communication inputs; assigning, based on application of the one or more weighted values, confidence levels to the communications inputs; determining which of the confidence levels are below a confidence threshold; executing one or more disambiguation rules to disambiguate the communication inputs with confidence levels below the confidence threshold; and generating a communication instruction to perform an action that is specified by the multi-dimensional communication.

Abstract: Systems and techniques for predictive data analytics are described. In a method for selecting a predictive model for a prediction problem, the suitabilities of predictive modeling procedures for the prediction problem may be determined based on characteristics of the prediction problem and/or on attributes of the respective modeling procedures. A subset of the predictive modeling procedures may be selected based on the determined suitabilities of the selected modeling procedures for the prediction problem. A resource allocation schedule allocating computational resources for execution of the selected modeling procedures may be generated, based on the determined suitabilities of the selected modeling procedures for the prediction problem. Results of the execution of the selected modeling procedures in accordance with the resource allocation schedule may be obtained. A predictive model for the prediction problem may be selected based on those results.

Abstract: A system and method provide for querying a knowledge base. The knowledge base includes a plurality of layers, each layer corresponding to a different type of information, such as user's intent, lack of knowledge, and specific issue. The system includes categorization rules for categorizing at least a part of a user's request according to the types of information and for mapping the request to respective layers of the knowledge base. The user's request is categorized with the categorization rules and mapped to one of the layers of the knowledge base, based on the categorization. A search is conducted for responsive problem descriptions among the problem descriptions in that layer. The system and method can thus provide for differential searching of the knowledge base, depending on the type of information being sought.

Abstract: An analysis device which analyzes a system that inputs input data including a plurality of input parameters and outputs output data, including an acquisition unit that acquires learning data including a plurality of sets of the input data and the output data, and a learning processing unit that learns, based on the acquired learning data, the amount of difference of output data corresponding to a difference between input parameters of two pieces of input data, an analysis method using the analysis device, and a program used in the analysis device are provided.

Abstract: The present disclosure relates to a method for performing automated discovery of new topics from unlimited documents related to any subject domain, employing a multi-component extension of Latent Dirichlet Allocation (MC-LDA) topic models, to discover related topics in a corpus. The resulting data may contain millions of term vectors from any subject domain identifying the most distinguished co-occurring topics that users may be interested in, for periodically building new topic ID models using new content, which may be employed to compare one by one with existing model to measure the significance of changes, using term vectors differences with no correlation with a Periodic New Model, for periodic updates of automated discovery of new topics, which may be used to build a new topic ID model in-memory database to allow query-time linking on massive data-set for automated discovery of new topics.

Abstract: A first indication from a user is received. The indication includes a task to be performed using at least one application programming interface. A machine learning model is determine. At least one application programming interface is determined using the machine learning model and the request. The at least one application programming interface is provided to the user.

Abstract: Provided in the present invention are a method and apparatus for labeling training samples. In the embodiments of the present invention, two mutually independent classifiers, i.e. a first classifier and a second classifier, are used to perform collaborative forecasting on M unlabeled first training samples to obtain some of the labeled first training samples, without the need for the participation of operators; the operation is simple and the accuracy is high, thereby improving the efficiency and reliability of labeling training samples.

Abstract: Remote computing resource service providers allow customers to execute virtual computer systems in a virtual environment on hardware provided by the computing resource service provider. The hardware may be distributed between various geographic locations connected by a network. The distributed environment may increase latency of various operations of the virtual computer systems executed by the customer. To reduce latency of various operations predictive modeling is used to predict the occurrence of various operations and initiate the operations before they may occur, thereby reducing the amount of latency perceived by the customer.

Abstract: Systems and methods for analyzing documents are provided herein. A plurality of documents and user input are received via a computing device. The user input includes hard coding of a subset of the plurality of documents, based on an identified subject or category. Instructions stored in memory are executed by a processor to generate an initial control set, analyze the initial control set to determine at least one seed set parameter, automatically code a first portion of the plurality of documents based on the initial control set and the seed set parameter associated with the identified subject or category, analyze the first portion of the plurality of documents by applying an adaptive identification cycle, and retrieve a second portion of the plurality of documents based on a result of the application of the adaptive identification cycle test on the first portion of the plurality of documents.

Abstract: Measurements of individual-level infectivity, susceptibility and baseline infection risk to biological or social contagion and sensitivity to environmental factors are made for large number of entities from their contact relation, sequential infection occurrences and environmental factors, using computer implemented Markov Chain Monte Carlo and likelihood maximization for Bayesian estimations of an integrated latent trait response model. The method is useful for precise and efficient contagion control and prevention.

Abstract: The systems, devices, articles, and methods generally relate to sampling from an available probability distribution. The samples maybe used to create a desirable probability distribution, for instance for use in computing values used in computational techniques including: Importance Sampling and Markov chain Monte Carlo systems. An analog processor may operate as a sample generator, for example by: programming the analog processor with a configuration of the number of programmable parameters for the analog processor, which corresponds to a probability distribution over qubits of the analog processor, evolving the analog processor, and reading out states for the qubits. The states for the qubits in the plurality of qubits correspond to a sample from the probability distribution. Operation of the sampling device may be summarized as including updating a set of samples to include the sample from the probability distribution, and returning the set of samples.

Abstract: Media content is recommended based on suitability for a designated activity. A vector engine is trained using a plurality of lists, each of the lists containing metadata associated with a plurality of media objects. The vector engine includes a neural network trained with corpus data including (i) the plurality of lists (ii) a plurality of titles, each one of the titles associated with one of the lists, and (iii) the metadata associated with the plurality of media objects. Training the vector engine involves initializing, using the vector engine, a plurality of feature vectors representing each of the lists, each of the media objects, and each of a plurality of words in the titles of the lists. The training then further involves nudging, using the vector engine, the feature vectors based on a plurality of co-occurrences of the lists, the media objects, the words in the titles of the lists, or a combination thereof. A feature vector corresponding to an activity is identified among the feature vectors.

Abstract: Techniques for model-based analysis of a data center. A method includes creating a metamodel based on domain knowledge to represent a type of object and/or relationship of a data center, using static and dynamic configuration and data analysis techniques to discover topology of elements of the data center and represent the topology as a model that is an instance of the metamodel, extending the metamodel, using the model to perform analysis of the data center in connection with a specified task, leveraging domain knowledge represented in nodes of the extended metamodel to guide the analysis in terms of determining guidelines to apply to each node and determining relationships to traverse to continue the analysis, extending the domain knowledge by updating the extended metamodel upon discovery of additional knowledge for use in improving analysis tasks, and extending the model on-demand using dynamic analysis techniques upon detection of multiple analysis failures.

Abstract: Systems and methods for performing analyses on data sets to display canonical rules sets with dimensional targets are disclosed. A cross-corpus rule set for a given Topic can be generated based on the entire corpus of data. A first dimensional rule set can be generated based on a first context (e.g., based on the same Topic but using a first sub-domain of the corpus of data). A second dimensional rule set can be generated based on a second context (e.g., based on the same Topic but using a second sub-domain of the corpus of data). Key dimensional differentiators (e.g., for each dimension, or context, of the Topic) can be determined based on a comparison of the general rule set, the first dimensional rule set, and the second dimensional rule set. A canonical rule set visualization can be displayed. The visualization can highlight the dimensional selectors (e.g., those tokens, or nodes, that differ between the first dimensional rule set and the second dimensional rule set).

Abstract: Methods, systems, computer-readable media, and apparatuses for inferring context are provided. In one potential implementation, first context information associated with a first duration is identified, second context information is accessed to determine a context segmentation boundary; and the first context information and the second context information is then aggregated to generate an inferred segmented aggregated context. In a further implementation, the first context information is used to average inferred contexts, and the context segmentation boundary is used to reset a start time for averaging the first context information.

Abstract: Described herein are embodiments of systems, methods, and computer programs for estimating affective response to a token instance of interest. A measurement of affective response of the user to a temporal window of token instances is taken. A token instance belonging to the window is selected as a token instance of interest based on an attention level of the user, and is removed from the temporal window of token instances. A predicted affective response to the temporal window of token instances from which the token instance of interest is removed is computed. The affective response to the token instance of interest is computed based on the difference between the measured affective response of the user and the predicted affective response.

Abstract: A user may be exposed to multiple token instances representing stimuli that may influence the affective state of the user. Described herein are embodiments of systems, method, and computer programs for estimating affective response to a token instance of interest, selected from among the token instances. The token instance of interest is selected based on attention levels derived from eye-tracking data. In one example embodiment, the token instance of interest is a token instance in which the attention of the user is higher than the attention level of the user in at least one other token instance to which the user is exposed at the same time.

Abstract: A system for establishing a hearing ability model of a hearing ability of a person, includes a data storage configured to store a representation of a distribution of a hearing ability of a population of individuals, and a processor configured to establish a hearing ability model representing a hearing ability of the person based at least in part on (i) information regarding a person's response to a stimulus of a hearing evaluation event, and (ii) the representation of the distribution of the hearing ability of the population.