Abstract: An information classification device (1) is provided with an union of sets determination unit (10) which performs correct/incorrect determination regarding a content to be classified using a union of sets rule, and an individual determination unit (11) which applies a plurality of individual determination rules to the content to be classified which has been determined as correct, determines whether the content matches the condition, and performs correct/incorrect determination again regarding the content to be classified which has been determined as correct on the basis of the determination result of each individual determination rule. The union of sets determination rule is created using a result of correct/incorrect determination previously performed by two or more people with respect to a plurality of contents which are different from the contents to be classified, and also using feature amounts of respective different contents.

Abstract: A parallel genetic algorithm computing process tracks forward progress of a first sub-population across generations thereof. The first sub-population is one of a plurality of sub-populations that form a population of candidate solutions to an optimization problem. At a current generation of the first sub-population, it is determined that forward progress of the first sub-population fails a set of one or more forward progress criteria. In response to determining that the forward progress of the first sub-population fails the set of one or more forward progress criteria at the current generation, a local catastrophe is invoked on the current generation of the first sub-population. The first sub-population is re-populated after the local catastrophe is invoked. The first sub-population is re-established after re-populating while constraining migration to the first sub-population.

Abstract: A parallel genetic algorithm computing process tracks forward progress of a first sub-population across generations thereof. The first sub-population is one of a plurality of sub-populations that form a population of candidate solutions to an optimization problem. At a current generation of the first sub-population, it is determined that forward progress of the first sub-population fails a set of one or more forward progress criteria. In response to determining that the forward progress of the first sub-population fails the set of one or more forward progress criteria at the current generation, a local catastrophe is invoked on the current generation of the first sub-population. The first sub-population is re-populated after the local catastrophe is invoked. The first sub-population is re-established after re-populating while constraining migration to the first sub-population.

Abstract: Disclosed are a method and an apparatus for transmitting a sparse signal, and a method and an apparatus for recovering the sparse signal. The method for recovering a sparse signal by using a sparse signal recovering device that recovers a target signal from a received signal includes receiving a measurement signal with a noise signal from a sparse signal transmitting device which scans a target signal based on a measurement matrix, performing a mutual update procedure in which a likelihood probability is calculated by using a posterior probability of the target signal based on a relation between the target signal and the measurement signal, and the posterior probability is updated by using the likelihood probability, and recovering the target signal by performing maximum a posterior estimation for a final posterior probability output through the mutual update procedure.

Abstract: The disclosed embodiments provide a reputation system. The reputation system includes a statistical model associated with a set of items and a set of dimensions of the items in the reputation system, wherein the statistical model is trained using a positive class and a negative class. The reputation system also includes a scoring apparatus that applies the statistical model to a set of features for each of the items to estimate a set of reputation scores for the items. Finally, the reputation system includes a ranking apparatus that enables use of the set of reputation scores in the reputation system.

Abstract: A method of performing real-time correction of a water stage forecast includes obtaining at least one predicted water stage of at least one time and a predicted water stage of a next time after the at least one time; obtaining at least one observed water stage of the at least one time; generating a system error of the water stage forecast according to the at least one observed water stage, the at least one predicted water stage, the predicted water stage of the next time, a Time Series method, and an Average Deviation method; utilizing a Kalman filter method to generate a random error of the water stage forecast; generating a water stage forecast correction of the next time according to the system error and the random error; and correcting a predicted water stage of the next time according to the water stage forecast correction and the predicted water stage.

Abstract: Future travel times of a target vehicle traveling on a route from a starting point to a destination are predicted by first acquiring, by a probe vehicle, real-time probe data to alternative links from the starting point to the destination. Then, the future travel time for each link is predicted using a set of regression functions.

Abstract: Systems and methods may provide traversing data using metadata. In one example, a method may include gathering a textual description of a first object, wherein the textual description includes a word, generating a vector represent the textual description, assigning a first weight value to the word, associating an object space with the word including assigning a second weight value to the word, and associating an object space with the first object.

Abstract: Systems and methods are provided for combining multiple segmentations into a single unique segmentation that contains attributes of the original segmentations. This new segmentation forms an ensemble or combination segmentation that has a unique set of attributes from the original segmentations without enumerating every possible set of combinations. In one example, two or more segments are combined into a single segmentation using a technique such as k-means clustering or Self-Organizing Map Neural Networks. After the first combination phase is performed, a Bayesian technique is then applied in a second phase to adjust or further alter the ensemble combination of segments.

Abstract: Provided herein are various systems and methods of using an exam type data structure to map exam types in various formats to master exam types that may be associated with customized rules or other features.

Abstract: A mechanism for automatic quantification of multimedia production quality is presented. A method of embodiments includes assembling data samples from users, the data samples indicating a relative production quality of a set of content items based on a comparison of production quality between content items in the set, extracting content features from each of the content items in the set, and learning, based on the data samples from the plurality of users, a statistical model on the extracted content features, wherein the learned statistical model can predict a production quality of another content item that is not part of the set of content items.

Abstract: A low-order model (LOM) of biological neural networks and its mathematical equivalents including the clusterer interpreter probabilistic associative memory (CIPAM) are disclosed. They are artificial neural networks (ANNs) organized as networks of processing units (PUs), Each PU comprising artificial neuronal encoders, synapses, spiking/nonspiking neurons, and a scheme for maximal generalization. If the weights in the artificial synapses in a PU have been learned (and then fixed) or can be adjusted by the unsupervised accumulation rule and the unsupervised covariance rule (or supervised covariance rule), the PU is called unsupervised (or supervised) PU. The disclosed ANNs, with these Hebbian-type learning rules, can learn large numbers of large input vectors with temporally/spatially hierarchical causes with ease and recognize such causes with maximal generalization despite corruption, distortion and occlusion.

Abstract: To provide an information spread scale prediction device capable of accurately predicting the number of future contributions for a specific topic in SNS and the like. The information spread scale prediction device includes: a learning text data input unit which acquires learning text data from a specific website; a node influence learning unit which calculates the influence for the number of statements by each group to which a node specifying a single specific user belongs for the topic from the number of statements by each classified topic, and stores it as learning data; a prediction text data input unit which acquires prediction text data from the specific website after storing the learning data; and a future contribution number prediction unit which predicts and outputs the number of contributions at a specific future time of the topic based on the number of statements of each topic and the learning data.

Abstract: A characteristic forecasting system is disclosed. The characteristic forecasting system may have a memory and a processor. The memory may store instructions, that, when executed, enable the processor to generate at least one chromosome using a genetic algorithm, the chromosome including data values for variables of one or more equations used to generate forecast data for a target item. The processor may also be enabled to calculate a chromosome value for the chromosome based on a goal function associated with the genetic algorithm and determine at least one process parameter value for the chromosome at a time interval of the forecast data. The processor may also compare the process parameter value to a process constraint value representing a process limitation associated with the target item and modify the chromosome value for the chromosome responsive to a determination that the process parameter value does not satisfy the process constraint value.

Abstract: Systems, methods, and other embodiments associated with decision making with analytically combined split conditions are provided. In one embodiment, a method for classifying data is provided. An input data sample is received for classification as belonging to one of two possible classes. The input data sample includes a set of attribute values. The method includes evaluating the set of attribute values with a tree function that defines a decision boundary of a classification tree. The tree function classifies an input data sample as belonging to one of the two possible classes based, at least in part, on the attribute values of the input data sample. In another embodiment parameters of the tree function are derived by applying a gradient descent parameter update rule to the training data samples.

Abstract: A business rules engine includes dynamic objects to support dynamically addressable facts. The business rules engine is configured to reduce the need for developer resources to provision resources and adjust/adapt input data and output which would traditionally be required. As a result significant cost advantages are provided in the use of a business rules engine.

Abstract: The invention provides a system and method for describing polymorphisms or genetic variants based on information about mutations and relationships among them. The invention uses object-oriented concepts to describe variants as variant objects and relations among those variants as variant relation object, each object being an instance of an abstract class of genomic feature and able to contain any number of other objects. Information about genetic disorders is stored in association with the object that represents the pathogenic variant. Genetic test results are used to access corresponding objects to provide a report based on variants or polymorphisms in a patient's genetic material.