Abstract: Disclosed is a prediction method for monitoring performance of power plant instruments. The prediction method extracts a principal component of an instrument signal, obtains an optimized constant of a SVR model through a response surface methodology using data for optimization, and trains a model using training data. Therefore, compared to an existing Kernel regression method, accuracy for calculating a prediction value can be improved.

Abstract: Methods and systems for predicting statistically probable systematically assigned identifiers are disclosed, as are methods and systems for determining the likelihood that a systematically assigned identifier provided by a purported assignee of the identifier is legitimate. In one example, determining the likelihood of legitimacy includes determining the likelihood that the provided identifier is a valid identifier and determining the likelihood that the provided identifier was assigned to the purported assignee. To accomplish this validation, the present disclosure employs patterns of assignment discernable for systematically assigned identifiers in combination with statistical methods.

Abstract: A method for learning a process behavior model based on a process past instances and on one or more process attributes, and a method for detecting an anomalous process using the corresponding process behavior model.

Abstract: A method for identifying emerging concepts in unstructured text streams comprises: selecting a subset V of documents from a set U of documents; generating at least one Boolean combination of terms that partitions the set U into a plurality of categories that represent a generalized, statistically based model of the selected subset V wherein the categories are disjoint inasmuch as each document of U is included in only one category of the partition; and generating a descriptive label for each of the disjoint categories from the Boolean combination of terms for that category.

Abstract: Methods and apparatuses for determining the identity of the user detect a current user's electronic device activity pattern; compare the detected activity pattern against a plurality of user action identification profiles, wherein each user action identification profile is associated with a particular user; and use the comparing to identify the current user as being the particular user.

Abstract: In the situation where an amount of information on a user action history is small, information which is unexpected and interesting to a user cannot be presented with high accuracy. In order to solve this problem, a general degree of unexpectedness of an item, which does not depend on the user action history, and a degree of preference of the user to the item, which is based on the user action history, are calculated in an integrated manner, whereby the information which is unexpected and interesting to the user is recommended.

Abstract: An apparatus and method are disclosed for a measurement system that reports as a measurement result a confidence interval associated with a histogram bin into which a measurement value falls. The confidence interval is calculated from a subset of training values that also fall within the histogram bin. A training process may be performed in which a plurality of training values is obtained and a mean and standard deviation of the values determined. A plurality of histogram bins are defined from the mean and standard deviation and, for the subsets of training values that fall into each bin, confidence intervals calculated. A need to perform the training process may be determined from a plurality of measured values.

Abstract: Described is a method for image registration utilizing particle swarm optimization (PSO). In order to register two images, a set of image windows is first selected from a test image and transformed. A plurality of software agents is configured to operate as a cooperative swarm to optimize an objective function, and an objective function is then evaluated at the location of each agent. The objective function represents a measure of the difference or registration quality between at least one transformed image window and a reference image. The position vectors representing the current individual best solution found and the current global best solution found by all agents are then updated according to PSO dynamics. Finally, the current global best solution is compared with a maximum pixel value which signifies a match between an image window and the reference image. A system and a computer program product are also described.

Abstract: A computer implemented method, apparatus, and computer usable program code for content prediction. Differences between a sample set of cells and a target set of cells are compared to identify a pattern based on regular expression analysis. A preview of content prediction changes is presented for a user selection. Content predictions changes are made to the user selection in response to receiving a preview acceptance accepting the preview.

Abstract: Described is a behavior recognition system for detecting the behavior of objects in a scene. The system comprises a semantic object stream module for receiving a video stream having at least two frames and detecting objects in the video stream. Also included is a group organization module for utilizing the detected objects from the video stream to detect a behavior of the detected objects. The group organization module further comprises an object group stream module for spatially organizing the detected objects to have relative spatial relationships. The group organization module also comprises a group action stream module for modeling a temporal structure of the detected objects. The temporal structure is an action of the detected objects between the two frames, whereby through detecting, organizing and modeling actions of objects, a user can detect the behavior of the objects.

Abstract: An exemplary methodology, procedure, system, method and computer-accessible medium can be provided to assign a joint-probability distribution to a set of continuous variables, given a sample of independent joint observations. The exemplary embodiments can transform at least a portion of particular data into transformed data, repeat the transformation until a marginal distribution of at least the portion of the particular data has been transformed to be at least sufficiently near or to reach a predetermined distribution, and estimate at least one likelihood of at least one predetermined result associated with the particular data as a function of the transformed data, whereas the repeated transformation ascends a likelihood function and/or is performed by unitary transformations.

Abstract: A system and method is disclosed for profiling subjects and objects based on subjects' responses to various objects for purposes of determining and presenting the objects most likely to generate the most positive response from each visitor. Object ratings, such as aesthetic response, preference, interest, or relevancy, are explicitly submitted by subjects or derived implicitly from visitor interactions with the objects. Objects include movies, books, songs, commercial products, news articles, advertisements or any other type of content or physical item. A profiling engine processes the ratings information and generates compact profiles of each subject and object based on the similarities and differences in affinities between the group of subjects and the group of objects. A recommendation engine then generates recommendations to a subject based on similarity between the subject and object profiles. The recommendation engine can also match subjects to other subjects and objects to other objects.

Abstract: A computer-aided lean management (CALM) controller system recommends actions and manages production in an oil and gas reservoir/field as its properties and conditions change with time. The reservoir/field is characterized and represented as an electronic-field (“e-field”). A plurality of system applications describe dynamic and static e-field properties and conditions. The application workflows are integrated and combined in a feedback loop between actions taken in the field and metrics that score the success or failure of those actions. A controller/optimizer operates on the combination of the application workflows to compute production strategies and actions. The controller/optimizer is configured to generate a best action sequence for production, which is economically “always-in-the-money.

Abstract: An information processing apparatus including: a label acquisition section that acquires a label assigned by a user to a content selected among plural contents; a user certainty factor setting section that sets a user certainty factor to the label assigned by the user; a label prediction learning section that performs label prediction learning; a label prediction section that predicts a label regarding a content to which the label is not assigned, and calculates a label certainty factor that refers to certainty of the predicted label; a user certainty factor prediction section that performs user certainty factor prediction learning, and predicts a user certainty factor of (regarding) the predicted label of (regarding) the content to which the label is not assigned; and a selection section that selects a content to be next assigned a label among contents to which labels are not assigned.

Abstract: A system for training classifiers in multiple categories through an active learning system, including a computer having a memory and a processor, the processor programmed to: train an initial set of m binary one-versus-all classifiers, one for each category in a taxonomy, on a labeled dataset of examples stored in a database coupled with the computer; uniformly sample up to a predetermined large number of examples from a second, larger dataset of unlabeled examples stored in a database coupled with the computer; order the sampled unlabeled examples in order of informativeness for each classifier; determine a minimum subset of the unlabeled examples that are most informative for a maximum number of the classifiers to form an active set for learning; and use editorially-labeled versions of the examples of the active set to re-train the classifiers, thereby improving the accuracy of at least some of the classifiers.

Abstract: According to an example embodiment, a technique may be provided for managing a label data base of which each entry comprises a data field for insertion into a respective packet in a multicast transmission and a validity field. The technique may include, for example, (a) establishing a fixed size for a cluster of labels, (b) associating with each cluster a pointer to another cluster, (c) establishing a sentinel cluster of which all entries are indicated to be invalid and of which the cluster pointer points to this sentinel cluster, (d) establishing a root table which defines the number of replications of the packet in a multicast group and identifies a first label cluster associated with the multicast group and (e) establishing a chain of clusters of which each cluster which is full of valid entries points to the next cluster in the chain and the last cluster which contains valid entries points to the sentinel cluster.

Abstract: Embodiments treat cyclical behaviors based on behavioral data describing physiological factors affecting the behaviors. According to various embodiments, a processor or probe produce, from the behavioral data, factor data concerning the factors. Using a processor or recommendation engine that analyzes the factor data, treatment data is recommended based on an estimate of how the at least one factor affects the at least one cyclical behavior. A processor or feedback engine determines, from the treatment data, behavioral feedback configured to produce new behavioral data and to harness the effects of the factors to improve the cyclical behaviors.

Abstract: A kernel-based method determines the similarity of a first spectrum and a second spectrum. Each spectrum represents a result of spectral analysis of a material or chemical and comprises a set of spectral attributes distributed across a spectral range. The method calculates a kernel function which makes use of the shape of the spectral response surrounding a spectral point. This is achieved by comparing the value of an spectral attribute in a spectrum and each of a set of neighboring spectral attributes within a window around the spectral attribute. Weighting values can be applied to calculations when deriving the kernel function. The weighting values can assign different degrees of importance to different regions of the spectrum. The method can be used to: classify unknown spectra; predict the concentration of an analyte within a mixture; database searching for the closest match using a kernel-derived distance metric; visualization of high-dimensional spectral data in two or three dimensions.

Abstract: A system and method for building a predictive score without model training are disclosed. A set of predictive variables is defined based on raw data fields generated from raw data from one or more sources and domain knowledge. The raw data includes a historical set of transactions previously generated by one or more raw data sources. An scaled relative risk table to describe each predictive variable of the set of predictive variables is generated. The set of predictive variables is combined based on their associated relative risk tables to generate a predictive score for a future set of transactions.

Abstract: A method for legal knowledge modeling and automated legal evaluation, such as for online, questionnaire-based legal analysis, is provided. Information, such as facts and characteristics of a legal situation or legal scenario, as it relates to a legal conclusion or a legal result, are modeled in a decision tree. The decision tree may comprise a plurality of nodes and a plurality of edges connecting the nodes, wherein each node is associated with a variable that represents either an answer to a legal inquiry or a legal conclusion, and an edge represents a conditional dependency between variables of nodes. The decision tree is automatically updated on a periodic basis to reflect new legislation or court decisions. Using the decision tree, a legal conclusion based on the user's answers to a questionnaire may be determined. The legal conclusion is modified upon the input of evidence, which is typically in the form of answers to a dynamic set of questions designed to identify a legal conclusion or a legal result.