Abstract: A method (and structure) of quantum computing. Two independent magnitudes of a three-state physical (quantum) system are set to simultaneously store two real, independent numbers as a qubit. The three-state physical (quantum) system has a first energy level, a second energy level, and a third energy level capable of being degenerate with respect to one another, thereby forming basis states for the qubit.

Abstract: A method for analyzing an electrical power system using fuzzy logic includes: (a) acquiring data representing a signal of interest of the power system; (b) analyzing the signal using at least one fuzzy logic rule, wherein the fuzzy logic rules include: (1) phase level rules which are based on features extracted from a particular phase on the electric power system; (2) segment level rules which are evaluated using information from different phases; and (3) capture level rules which combine truth values obtained from one or more segments; and (c) based on the analysis, detecting and classifying at least one power system event within the power system.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for score normalization. One of the methods includes receiving initial training data, the initial training data comprising initial training records, each initial training record identifying input data as input and a category as output. The method includes generating a first trained predictive model using the initial training data and a training function. The method includes generating intermediate training records by inputting input data of the initial training records to a second trained predictive model, the second trained predictive model generated using the training function, each intermediate training record having a score. The method also includes generating a score normalization model using a score normalization training function and the intermediate training records.

Abstract: Apparatuses and methods to perform pattern matching are presented. In one embodiment, an apparatus comprises a memory to store a first pattern table comprising information indicative of whether a byte of input data matches a pattern and whether to ignore other matches of the pattern occur in remaining bytes of the input data. The apparatus further comprises one-byte match logic coupled to the memory, to determine, based on the information in the first pattern table, a one-byte match event with respect to the input data. The apparatus further comprises a control unit to filter the other matches of the pattern based on the information of the first pattern table.

Abstract: Providing for local graph partitioning using an evolving set process is disclosed herein. By way of example, a computer processor can be configured to execute local partitioning based on evolving set instructions. The instructions can be employed to transition a set of analyzed vertices of a graph until a segment of the graph with small conductance is identified. A transitioning algorithm can expand or contract the analyzed set of vertices based on characteristics of vertices at a boundary of the analyzed set. Accordingly, as the set of analyzed vertices becomes large, significant processing efficiency is gained by employing the characteristics of boundary vertices to transition the set or determine conductance, rather than all vertices of the analyzed set.

Abstract: In some implementations, a plurality of first questions and corresponding first answers are identified at a community question-answer (CQA) site as a plurality of first question-answer (q-a) pairs. A query thread comprised of a second question and a plurality of candidate second answers is selected for making a determination of answer quality. A set of the first questions that are similar to the second question are identified from the plurality of first questions. First linking features between the identified set of first questions and their corresponding first answers are used for determining an analogy with second linking features between the second question and candidate answers for ranking the candidate answers.

Abstract: An epithelial detector and method for automatically identifying epithelial portions of a tissue sample, includes: staining the tissue sample with at least two dyes; applying a color transformation to a color image of the tissue sample to obtain one or more color channels; and applying a trained convolutional neural network to the color channels to obtain a decision for position in the tissue as to whether it is inside or outside an epithelial layer. Also, a method for training the convolutional neural network.

Abstract: A user state presumption system and method determines whether a user has exhibited a specific intended behavior that is an aim of service(s) provided by analyzing data of a series of accesses of the user made relative to the service(s). An intended behavior probability calculator is provided which calculates, every predetermined period, a proportion of users who have exhibited the intended behavior among the plurality of users and records the obtained proportion calculated to implement the user state presumption system and method.

Abstract: A method and system using a processor for reasoning an optimized service of a ubiquitous system using context information and emotion awareness. The system using a processor for reasoning an optimized service of a ubiquitous system includes: a ubiquitous system unit to collect information about surroundings of a user; a context awareness unit to recognize a state of the user based on the information collected in the ubiquitous system unit, and to generate context information; an emotion awareness unit to recognize an emotion of the user based on the information collected in the ubiquitous system unit, and to generate emotion information; a combination unit to combine the context information and the emotion information; and a service selection unit to reason and select a service for the user based on the combination result of the combination unit.

Abstract: In accordance with one aspect of the present exemplary embodiment, a system determines a solution based on received data. An intake component receives an incomplete data set from one or more sources. A recommendation system transforms the incomplete data set into a semantic data set via latent semantic indexing, classifies the semantic data set into an existing cluster and provides one or more solutions of the existing cluster as one or more recommendations.

Abstract: The invention is a system and method for teaching and using a computer to apply a genetic algorithm in order to generate rules for classifying data objects, such as documents. By permitting periodic human intervention during and/or after the initial training stage, the invention improves upon existing methods that merely automate data classification. Using an initial “training set” of data objects, the system develops a set of rules to apply on future data objects. To improve efficacy over time, rules may be periodically amended by a user during the initial training stage or after rules have been generated. In this fashion, classification rules may evolve, thereby improving the accuracy and speed of the classification process. Classification rules are also presented in a human-readable format, thereby improving the transparency of the classification process and facilitating the interaction between the system and the administrator.

Abstract: A technique for analyzing a model, either statically or dynamically, checks the model for one or more specified patterns with respect to accessing a data store associated with the model. The patterns may include, for example, writing to the data store prior to reading the data store, reading the data store prior to writing to the data store, writing to the data store multiple times prior to reading the data store, reading the data store multiple times before writing to the data store, etc. The model may be an executable graphical model that is generated in a graphical modeling environment. A result may be generated based on analyzing the model and the result may be output.

Abstract: A method for monitoring a system includes receiving a first data asset from the system, receiving a configuration file, determining whether the configuration file includes the first data asset and an associated first rule engine logic identifier, and running a first rule engine associated with the first rule engine logic identifier responsive to determining that the configuration file includes the first data asset and the associated first rule engine logic identifier. The method also includes sending the first data asset to the first rule engine, receiving a first result from the first rule engine, and sending the first result to a display for presentation to a user.

Abstract: A system and method for inductive and analogical reasoning provide capabilities for generalization, creative reasoning, possibilistic analysis, massive parallelism, and distributed processing. The system and method involve using matched cases/generalizations/analog inferences from a case base or generalization/analog base to provide output inferences based upon a user-supplied context. A user then provides feedback to indicate that the output inference is either correct or incorrect. If the user indicates that the output is incorrect, new rules may be computer-generated during runtime by performing, for example, an analogical contextual transformation. If the user indicates that the output is correct, the matched case/generalization/analog may be moved to the head of its respective base.

Abstract: A method for researching and developing a recognition model in a computing environment, including gathering one or more data samples from one or more users in the computing environment into a training data set used for creating the recognition model, receiving one or more training parameters defining a feature extraction algorithm configured to analyze one or more features of the training data set, a classifier algorithm configured to associate the features to a template set, a selection of a subset of the training data set, a type of the data samples, or combinations thereof, creating the recognition model based on the training parameters, and evaluating the recognition model.

Abstract: Components of a distributed computing system are monitored, the components including hardware components and software components that operate on the hardware components. At least one of the software components is a service that includes a service level agreement. Performance characteristics of the components are determined based on the monitoring. The performance characteristics of the service are compared to the service level agreement to determine whether the service level agreement has been violated. At least one of the service or an additional service collocated with the service is migrated based on the performance characteristics of the components if the service level agreement has been violated.

Abstract: A decision making unit suitable for an autonomous platform. The decision making unit includes a receiving module configured to receive at least one ability request and a preconditioning module configured to precondition the received ability request. A filter is configured to filter the preconditioned ability request. A first generator is configured to generate a set of behavior instructions associated with the filtered ability request. A priority processor is configured to priority process the filtered ability request. A second generator is configured to generate at least one control signal depending upon the priority processing of the filtered ability request, wherein the control signal includes information about the generated set of behavior instructions.

Abstract: A method and system for truth discovery may implement a methodology that accounts for accuracy of sources and dependency between sources. The methodology may be based on Bayesian probability calculus for determining which data object values published by sources are likely to be true. The method may be recursive with respect to dependency, accuracy, and actual truth discovery for a plurality of sources.

Abstract: A database includes a list of members of a first group, a list of members of a second group, and ratings for at least some of the members of the second group. The database is accessed. The ratings are attributed to the members of the first group. A machine learning training set is built for a particular member of the first group. The training set includes class labels corresponding to the particular member's ratings for the members of the second group, and features that include supplied and predicted ratings from at least a subset of processed members of the first group. A predictor for the particular member of the first group is trained based on the machine learning training set. The predictor corresponding to the particular member is used to generate predicted ratings for one or more members of the second group the particular member has not rated.

Abstract: A dynamic constraint solver solves a constraint satisfaction problem that includes a problem having zero or more ports, and zero or more sub-problems coupled to the zero or more ports. The solver stores a cross problem constraint template for cross problem constraints. The solver receives a request to connect a first sub-problem to a first port of a first problem. The solver then determines if the first sub-problem forms a participating problem combination for the cross problem constraint template. The participating problem combination includes all participating problems of the cross problem constraint. The solver then generates the cross problem constraint for the participating problem combination if the first sub-problem forms the participating problem combination.