Abstract: A learning management system includes a content storage unit for storing learning content, a user modeling unit in signal communication with the content storage unit and having a user model, a personalization unit in signal communication with the content storage unit for personalizing the learning content stored in the content storage unit in response to the user model, and a user interface in signal communication with the content storage unit for enabling a user to interact with the learning management system, wherein the learning management system delivers content responsive to user interaction with the learning management system.

Abstract: A method and systems that allows building cause effect tree related to initial problem statement. Each event of cause effect tree can be added with additional axes: hierarchy and operation. Wizards and templates provide simplify adding of undesirable effects. System automatically generates alternative problem statements and queries for search in knowledge bases.

Abstract: A system provides the user with a simulated environment that presents a business opportunity to understand and solve optimally. Mistakes are noted and remedial educational material presented dynamically to build the necessary skills that a user requires for success in the business endeavor. The system utilizes an artificial intelligence engine driving individualized and dynamic feedback with synchronized video and graphics used to simulate real-world environment and interactions. A robust business model provides support for realistic activities and allows a user to experience real world consequences for their actions and decisions. An accounting tutorial system is enabled for providing active coaching on aspects of cost accounting including debit and credit processing, procedures for closing books, appropriate management of ledgers, assets and liabilities utilizing a t-account motif.

Abstract: Classification of objects using the best boolean expression that represents the most optimal combination of the underlying features is disclosed.

Abstract: Systems and methods are provided for fusing new evidence and displaying node parameters of a decision network. The decision network can be a belief network, such as a Dempster-Shafer belief network. The Dempster-Shafer belief network includes node parameters that conform to the Dempster-Shafer combination rule, which is based on an evidential interval: the sum of a belief value, a disbelief value and an unknown value is equal to one. A user override is provided to allow a user to override node parameters associated with a hypothesis or outcome and the decision network self-adjusts the appropriate link values or learned to instantiate the override. The back-propagation algorithm is used to adjust the links.

Abstract: A feature selection technique for support vector machine (SVM) classification makes use of fast Newton method that suppresses input space features for a linear programming formulation of a linear SVM classifier, or suppresses kernel functions for a linear programming formulation of a nonlinear SVM classifier. The techniques may be implemented with a linear equation solver, without the need for specialized linear programming packages. The feature selection technique may be applicable to linear or nonlinear SVM classifiers. The technique may involve defining a linear programming formulation of a SVM classifier, solving an exterior penalty function of a dual of the linear programming formulation to produce a solution to the SVM classifier using a Newton method, and selecting an input set for the SVM classifier based on the solution.

Abstract: Methods and systems are disclosed herein in which a physical neural network can be configured utilizing nanotechnology. Such a physical neural network can comprise a plurality of molecular conductors (e.g., nanoconductors) which form neural connections between pre-synaptic and post-synaptic components of the physical neural network. Additionally, a learning mechanism can be applied for implementing Hebbian learning via the physical neural network. Such a learning mechanism can utilize a voltage gradient or voltage gradient dependencies to implement Hebbian and/or anti-Hebbian plasticity within the physical neural network. The learning mechanism can also utilize pre-synaptic and post-synaptic frequencies to provide Hebbian and/or anti-Hebbian learning within the physical neural network.

Abstract: The invention concerns a method for determining competing risks for objects following an initial event based on previously measured or otherwise objectifiable training data patterns, in which several signals obtained from a learning capable system are combined in an objective function in such a way that said learning capable system is rendered capable of detecting or forecasting the underlying probabilities of each of the said competing risks.

Abstract: A Lagrangian support vector machine solves problems having massive data sets (e.g.

Abstract: The invention relates to data packet filtering and finding a rule matching a data packet in a rule base. A data packet comprises parameter fields for identifying the data packet, the rule base comprises a plurality of rules, each rule comprises one or more parameter fields, and the matching rule is a rule, whose parameter field values correspond to the parameter field values of said data packet. The matching rule is found by determining rule sets for the data packet, one rule set comprising the rules to which one parameter field value of the data packet can match, and by finding the rule with the smallest label that is present in all said rule sets of the data packet, said rule with the smallest label indicating the rule matching the data packet. Additionally, the invention relates to finding an element with the smallest label that is present in a plurality of finite subsets containing finite number of elements, said subsets being subsets of a set containing finite number of sequentially labelled elements.

Abstract: The present invention provides a method for implementing Q&A function for an electronic document, a method for computer-aided authoring, a method for browsing an electronic document, a computer-aided authoring apparatus, a browser capable of providing Q&A function, a method for providing Q&A service utilizing computers and a system for providing Q&A service. Said method for implementing Q&A function for an electronic document includes: when the writer is writing an electronic document, generating Q&A information used for Q&A function so that the reliability of the generated Q&A information is ensured by the writer; saving said Q&A information in correspondence with said electronic document; and using said Q&A information for providing Q&A function.

Abstract: An information processing method and an information processing apparatus in which the learning efficiency may be improved and the scale may be extended readily. An integrated module 42 is formed by a movement pattern learning module by a local expression scheme. The local modules 43-1 to 43-3 of the integrated module 42 are each formed by a recurrent neural network as a movement pattern learning model by a distributed expression scheme. The local modules 43-1 to 43-3 are caused to learn plural movement patterns. Outputs from the local modules 43-1 to 43-3, supplied with preset parameters, as inputs, are multiplied by gates 44-1 to 44-3 with coefficients W1 to W3, respectively, and the resulting products are summed together and output.

Abstract: To deal with user network communication activity which cannot easily and clearly be determined as problematic behavior, a behavior analysis apparatus 14 monitors communication between each user PC 16 in a domain 10 and Internet 20 via a gateway apparatus 12. For example, when there is a monitored item related to information leakage of the user in the detected communication, a weight value corresponding to the monitored item is added to a score concerning a possibility of the user leaking information. Subsequently, the scores are totaled and recorded for each unit of time. The behavior analysis apparatus 14 inputs data of time-series transition of the total value to a neural network which has performed learning for prediction processing, and predicts the possibility of the user's information leak at a time in the near future. When an increasing risk of leakage is predicted, the behavior analysis apparatus 14 communicates an alarm to a security manager.

Abstract: An ordering apparatus receives unit information for specifying units constituting a composite apparatus to be ordered. The ordering apparatus creates composite state information for specifying a composite state of units based on the received unit information, according to a predetermined rule. Meanwhile, the composite apparatus recognizes unit information for specifying units constituting itself, and creates composite state information based on the recognized unit information, according to the same rule as the above rule. Then, the composite state information created by the ordering apparatus and the composite state information created by the composite apparatus are compared. Accordingly, it is possible to realize a managing system capable of appropriately managing a complicated composite apparatus at the time of ordering, shipment, setup, subsequent maintenance, etc.

Abstract: A data mining system for a database management system and method and computer program product therefore, that provides improved functionality over synchronous data mining systems, and which provides features such as interruptible tasks and status output. A data mining system for a database management system comprises a plurality of data mining task objects operable to perform data mining functions, a data mining task queue table operable to maintain at least one queue to manage execution of the data mining tasks, and a data mining system task monitor operable to monitor execution of currently executing data mining tasks, examine the data mining task queue table, select at least one task for execution, dequeue data mining tasks from the data mining task queue table, and initiate execution of the dequeued tasks.

Abstract: A method is described for improving the prediction accuracy and generalization performance of artificial neural network models in presence of input-output example data containing instrumental noise and/or measurement errors, the presence of noise and/or errors in the input-output example data used for training the network models create difficulties in learning accurately the nonlinear relationships existing between the inputs and the outputs, to effectively learn the noisy relationships, the methodology envisages creation of a large-sized noise-superimposed sample input-output dataset using computer simulations, here, a specific amount of Gaussian noise is added to each input/output variable in the example set and the enlarged sample data set created thereby is used as the training set for constructing the artificial neural network model, the amount of noise to be added is specific to an input/output variable and its optimal value is determined using a stochastic search and optimization technique, namely, gen

Abstract: A quantum approach to the economically significant n-player public goods or similar n-player game requires only two-particle entanglement and is thus much easier to implement than games requiring n-particle entanglements. Two-particle entanglements are sufficient to give near optimal expected payoff when players use a simple mixed strategy for which no player can benefit by making different choices. This mechanism can also address some heterogeneous preferences among the players. Quantum games in accordance with the invention can be simulated on classical computers without requiring impractical amounts of processing power for large numbers of players.

Abstract: A monitoring system including a baseline model that automatically captures and models normal system behavior, a correlation model that employs multivariate autoregression analysis to detect abnormal system behavior, and an alarm service that weights and scores a variety of alerts to determine an alarm status and implement appropriate response actions. The baseline model decomposes the input variables into a number of components representing relatively predictable behaviors so that the erratic component e(t) may be isolated for further processing. These components include a global trend component, a cyclical component, and a seasonal component. Modeling and continually updating these components separately permits a more accurate identification of the erratic component of the input variable, which typically reflects abnormal patterns when they occur.

Abstract: A system and method for generating a neural network ensemble. Conventional algorithms are used to train a number of neural networks having error diversity, for example by having a different number of hidden nodes in each network. A genetic algorithm having a multi-objective fitness function is used to select one or more ensembles. The fitness function includes a negative error correlation objective to insure diversity among the ensemble members. A genetic algorithm may be used to select weighting factors for the multi-objective function. In one application, a trained model may be used to produce synthetic open hole logs in response to inputs of cased hole log data.

Abstract: The present invention relates to a method and apparatus, based on the use of a neural network (NN), for (a) predicting important groundwater/surface water output/state variables, (b) optimizing groundwater/surface water control variables, and/or (c) sensitivity analysis, to identify physical relationships between input and output/state variables used to model the groundwater/surface water system or to analyze the performance parameters of the neural network.