Abstract: The present disclosure includes a system and method for learning (or discovering and extracting) business knowledge from a collection of source code. The collection of source code is abstracted to generate an abstracted data stream, which is then transformed to an Extensible Markup Language (XML) format. The transformed data in XML format can be further converted to target formats or processed to satisfy different needs such as software system documentation, migration, impact analysis and security analysis. The disclosure also includes an implementation and operation for a pattern abstraction engine configured to receive an input data stream and format it for abstraction into a standard format using a pattern matching mechanism. The disclosure also includes an implementation and operation for a contextual pattern decoder engine configured to extract knowledge attributes and contextual taxonomy from classified blocks of an input data stream.

Abstract: Methods, systems, and apparatus, including medium-encoded computer program products, for detection of cheating on a cognitive test. In one aspect, a method includes receiving first information concerning a person, the first information specifying the person's responses, and lack thereof, for items of a cognitive test administered to the person, wherein the cognitive test includes multiple item-recall trials used to assess cognitive impairment; analyzing the first information using a classification algorithm trained on second information concerning a group of people to whom the cognitive test has been administered, the classification algorithm generated in accordance with a first part and a second part, the first part distinguishing between cheaters and non-cheaters, and the second part distinguishing between impaired cheaters and non-impaired cheaters; and identifying, based on the analyzing, the person as a cheater requiring a verification test to confirm an initial result of the cognitive test.

Abstract: One embodiment accesses a binary decision diagram (BDD) representing a function having n variables, where n?2, wherein: the BDD comprises n layers corresponding to the n variables, respectively; and the BDD has a first variable order where each variable i is at layer i for 1?i?n; and reorders the n variables of the BDD according to a second variable order denoted as ?(i), where each variable i is at layer ?(i) for 1?i?n, by iteratively and alternatingly swapping one or more first disjoint pairs of consecutive layers during each odd iteration and swapping one or more second disjoint pairs of consecutive layers during each even iteration, until the second variable order is achieved, wherein during each iteration, two consecutive layers are swapped only if a current order of two variables at the two consecutive layers differs from an order of the two variables specified by the second variable order.

Abstract: A system that incorporates representational authenticity into virtual world interactions can include user representational authenticity data and a virtual world computing system. The user representational authenticity data can be used to describe the physical characteristics of a human user. The physical characteristics can be quantified utilizing standardized measurement techniques. The virtual world computing system can be configured to adjudicate the conduction of interactions performed by a virtual representation of the human user within the virtual world environment utilizing the user representational authenticity data.

Abstract: Embodiments of the present invention analyze information available from social-media websites to predict the likelihood that a prospective candidate will become an active recruitement candidate.

Abstract: Some embodiments of a multiple entry point network for stream support in an exemplary rule engine have been presented. In one embodiment, a stream of events is asserted into a working memory of a rule engine, which supports event processing. The rule engine, running on a server, processes the stream of events against a set of rules retrieved from a rule repository of the rule engine. To process the events, the rule engine may construct a network having multiple root nodes, each being an entry point into the network, through which the events may enter the network and propagate through the network.

Abstract: In a system for evaluating classification systems such as an operating system (OS) fingerprinting tool (e.g., Nmap), information gain is used as a metric to evaluate the quality of the tool's classification tests, including fingerprinting tests and their associated probes. Information gain is determined using the OS fingerprinting tool's signature database rather than raw training samples, including taking into account signatures/data that are represented by ranges of test values, disjunctive values, and missing values. Uniform distributions over test values and classifications are assumed in applying these methods to an example signature database for Nmap. Other assumptions or a priori information (e.g., normal distributions over ranges) can also be accommodated. The information gain measure provided can be applied to other classification problems as well.

Abstract: A system for transforming domain specific unstructured data into structured data including an intake platform controlled by feed back from a control platform. The intake platform includes an intake acquisition module for acquiring data building baseline data related to a domain and problem of interest, an intake pre-processing module, an intake language module, an intake application descriptors module, and an intake adjudication module. The control platform includes a control data acquisition module, a control data consistency collator, a control auditor, a control event definition and policy repository, an error resolver, and an output that outputs results of the workflow into structured data enabled to be used in data analytics.

Abstract: A method and apparatus for testing the functionality of a circuit design uses working system data that is recorded in real-time to stimulate and/or verify a software simulation of the circuit design that does not run in real-time. In a selected embodiment, a system for simulating and verifying a software model of a baseband module circuit design is described in connection with using real-time input and output data captured from a corresponding circuit in a reference platform. The captured real-time data may include digital baseband I/Q samples and/or extracted control data pertaining to the signal level, channel frequency, gain, output power, frequency offset, DC offset, or the like. The captured data may be regenerated for use as a stimulus for the software model of the circuit design and/or to verify the functionality of the design.

Abstract: An Augmented Lagrangian Pattern Search (ALPS) algorithm that attempts to solve a non-linear optimization problem subject to non-linear, linear, and, bound constraints is discussed. The present invention utilizes information from the linear and bound constraints, formulates sub-problems using Lagrange parameter estimates and appropriate penalty parameters (using a log barrier), and provides a robust update formulae for parameters which guides the algorithm towards a minimum. The present invention solves a general non-linear optimization problem without using any slack variables to convert the inequality constraints to equality constraints or equality constraints to inequality constraints.

Abstract: Described is a system for multi-layered object detection which presents a unified way of processing an entire field-of-view (FOV) using cognitive swarms of software agents and classifier cascades by partitioning the FOV into layers and processing the closest layer first. A plurality of software agents operate as a cooperative swarm to search the first layer of the field-of-view to locate an objective function optima according to particle swarm optimization dynamics, wherein the objective function optima corresponds to a location of an object in the image in a layer of the field-of-view. The other layers are then sequentially swept to detect other objects in the FOV. In another aspect, the layers correspond to layers of increasing resolution in a hierarchical image pyramid. By using the cooperative swarm to search the coarser resolution layers first, objects can be detected more rapidly. A method and computer program product are also described.

Abstract: A system for creating an album is disclosed. In one embodiment, the system includes an image input configured to receive related images, an image memory coupled to image input, an image processor coupled to image memory, and an album output coupled to image processor. The image processor is configured to use game play with users to assign metadata labels to the related images and assign album layout rules based upon the story schema and metadata labels.

Abstract: The computerized method for identifying Hammerstein models is a method in which the linear dynamic part is modeled by a space-state model and the static nonlinear part is modeled using a radial basis function neural network (RBFNN). Accurate identification of a Hammerstein model requires that output error between the actual and estimated systems be minimized. Thus, the problem of identification is an optimization problem. A hybrid algorithm, based on least mean square (LMS) principles and the Subspace Identification Method (SIM) is developed for the identification of the Hammerstein model. LMS is a gradient-based optimization algorithm that searches for optimal solutions in the negative direction of the gradient of a cost index. In the method, LMS is used for estimating the parameters of the RBFNN. For estimation of state-space matrices, the N4SID algorithm for subspace identification is used.

Abstract: A system and method for controlling information output based on user feedback about the information is provided. At least one neural network module selects one or more of a plurality of objects to receive information from a plurality of information sources based at least in part on a plurality of inputs and a plurality of weight values during an epoch. The information sources may include electronic mail providers, chat participants, or page links. Recipients of the objects provide feedback about the information during an epoch. At the conclusion of an epoch, the neural network takes the feedback and generates a rating value for each of the plurality of objects. Based on the rating value and the selections made, the neural network redetermines the weight values within the network. The neural network then selects the objects to receive information during a subsequent epoch using the redetermined weight values and the inputs for that subsequent epoch.

Abstract: Embodiments of the invention provide methods and apparatus to use ray-tracing to perform artificial intelligence path-finding. Embodiments of the invention may cast a ray into a three-dimensional scene to determine a path from a starting point to an ending point. The ray may have a trajectory which originates at the starting point and proceeds towards the ending point. The artificial intelligence system may perform ray-tracing using a spatial index to determine if the ray intersects any objects within the three-dimensional scene. If the ray reaches the ending point without intersecting any objects, the path of the ray from the starting point to the ending point is the determined path. However, if the ray intersects an object, the artificial intelligence system may cast additional rays into the three-dimensional scene and perform ray tracing with the additional rays to determine a path from the start point to the end point.

Abstract: A method, system, and computer program product for building a classifier based on the concepts similar to a Bayesian classifier, but using fuzzy theory provides better understanding and an improved approach to modeling the data. The concept of prior information about the class is eliminated, and focuses on the likelihood function (obtained from training data). This is modeled as fuzzy sets. The process of classification uses the possibility-probability transformation. A method of performing data analysis processing includes constructing a fuzzy likelihood by modeling a likelihood of data given a training class using fuzzy sets and transforming the fuzzy likelihood into a probability distribution using a possibility-probability transformation.

Abstract: A network inference and validation engine is presented which combines data of different types into a network associations' inference and performs validation of existing networks based on constraints from several data sets or previously known linkages. The engine would assist scientists to integrate information from various sources into a network of association, validate previously known associations against the supplied constraint data sets and prune or modify existing connections based on subsequent experiments.

Abstract: Techniques for supporting generalized motion recognition are disclosed. A set of motion recognizers created from training sets of labeled processed motion signals is provided, each of the component outputs is transformed. When a motion signal including two or more component outputs is received, the component outputs are transformed into device-independent motion signals, where each of the component outputs describes a different component of a motion made by a user. The motion recognizers are applied to the motion signal to build generalized motion recognizers responsive to the motion sensitive device that has generated the motion signal.

Abstract: Described is a signal processing system. The system comprises a signal processing module having signal processing parameters and being configured to receive a plurality of signals. The signal processing module uses the signal processing parameters to output a processed signal, as either a fused signal or a plurality of separate signals. A classification module is included to recognize information encoded in the processed signal to classify the information encoded in the process signal, with the classification having a confidence level. An optimization module is configured, in a feedback loop, to utilize the information encoded in the processed signal to adjust the signal processing parameters to optimize the confidence level of the classification, thereby optimizing an output of the signal processing module.

Abstract: An information processing apparatus includes a target problem acquisition unit for acquiring a target problem, a generation unit for generating a plurality of solution candidates to the target problem to solve the target problem, and a contribution ratio calculating unit for calculating a contribution ratio of each solution candidate to the target problem if the target problem is solved using all the plurality of solution candidates to the target problem acquired by the target problem acquisition unit. The generation unit generates a solution candidate to the target problem in a next generation in accordance with a genetic algorithm that uses an evaluation value that is calculated using at least the contribution ratio of each solution candidate to the target problem determined by the contribution ratio calculating unit.