Abstract: Described is a system and method for determining a classification of an application that includes initiating a stress test on the application, the stress test including a predetermined number of stress events, wherein the stress events are based on a network impairment. A response by the application to each stress event is identified and the application is classified as a function of the response into one of a first classification and a second classification, the first classification indicative of a normal application and the second classification indicative of an undesired application. If, the application is in the second classification, a network response procedure is executed.

Abstract: A method for dynamically selecting string analysis algorithms can begin with the training of the dynamic string analysis handler of a string analysis module to effectively handle a subset of string queries having contextual metadata received from a client application in an instructional environment. The effectiveness of the training module can be based upon feedback from the client application. Upon completion of the training, a string analysis algorithm selection policy can be synthesized. The string analysis algorithm selection policy can correlate a context of a string query in the subset to the usage of a string analysis algorithm. When in the operational environment, the dynamic string analysis handler can dynamically handle string queries having contextual metadata received from the client application in accordance with the string analysis algorithm selection policy. The string analysis algorithm to be used for a string query can be dynamically and independently determined.

Abstract: The present invention is directed to a method of operating a knowledge capture program. The knowledge capture program has the steps of starting the knowledge capture program wherein a user can access content that is either existing content or creating new content. Once the content has been selected then a source subject matter can be selected and displayed, and then captured and incorporated into the content. The source subject matter can be edited and saved into the content. The content can then be retrieved and played in a desired mode of learning.

Abstract: A rules engine can be initialized as part of an object grid, wherein the rules engine is executable code executing using computer resources. The rules engine updates a business mapper. The rules engine receives a business rule definition to form a business rule. The rules engine determines business terms are present in the business rule. The rules engine converts the business terms to technical terms based on the business mapper, responsive to a determination that business terms are present. The rules engine determines that the business rule comprises a temporal rule. The rules engine receives rule criteria, wherein the rule criteria is at least one selected from the group consisting of frequency, delay and timing dependency, responsive to the determination that the business rule is a temporal rule. The rules engine stores the business rule to the rules engine. The rules engine publishes the business rule to a publish-subscribe topic.

Abstract: An information processing apparatus includes an obtainment section that obtains, via a network, information updated at a distribution origin on the network; and a determination section that determines an obtainment rule relating to a timing of the obtainment by the obtainment section of update information for the distribution origin, wherein the obtainment section obtains update information based on a predetermined learning rule for a first distribution origin for which obtainment rule has not been determined by the determination section, the determination section determines an obtainment rule for the first distribution origin based on a result of the obtainment by the obtainment section of update information from the distribution origin based on the learning rule, and the obtainment section, in response to the determination of the obtainment rule by the determination section, obtains update information from the first distribution origin based on the obtainment rule.

Abstract: A neural system comprises multiple neurons interconnected via synapse devices. Each neuron integrates input signals arriving on its dendrite, generates a spike in response to the integrated input signals exceeding a threshold, and sends the spike to the interconnected neurons via its axon. The system further includes multiple noruens, each noruen is interconnected via the interconnect network with those neurons that the noruen's corresponding neuron sends its axon to. Each noruen integrates input spikes from connected spiking neurons and generates a spike in response to the integrated input spikes exceeding a threshold. There can be one noruen for every corresponding neuron. For a first neuron connected via its axon via a synapse to dendrite of a second neuron, a noruen corresponding to the second neuron is connected via its axon through the same synapse to dendrite of the noruen corresponding to the first neuron.

Abstract: A method of deriving a classifier for classifying items using a plurality of variables for characteristics of the items, the method comprising determining a representative subset of the variables for use in said classifier.

Abstract: The present invention is directed towards systems and methods for predicting one or more desired properties of external nodes or properties of their relations with internal nodes, based on a selected group of nodes about which it is known whether the nodes have the desired properties, or it is known whether they have a desired relation property with an internal node. The method comprises storing in one or more data structures a first data set regarding external nodes and a second data set regarding nodes with known properties in a selected group, each data set having one or more data items representing one or more events relating to or attributes of each node in the data set, the second data set including one or more types of data items not included in the first data set.

Abstract: A query processing system is described herein for personalizing results for a particular user. The query processing system operates by receiving a query from a particular user u who intends to find results that satisfy the query with respect to a topic Tu, the user being characterized by user information ?u. In one implementation, the query processing system then produces a generic topic distribution Prr(T|q) associated with the query that is germane to a population of generic users, as well as a user-specific query-dependent topic distribution Pr(Tu|q,?u) for the particular user. The query processing system then produces personalized results for the particular user based on Prr(T|q) and Pr(Tu|q,?u). The query processing system can use multiple techniques to produce Pr(Tu|q,?u), such as, in one approach, a discriminative learning approach.

Abstract: A contingency planning system apparatus including a world description subsystem responsive to data representing a description of the world related to a planning problem and configured to instantiate a plurality of world codelets in a codelet workspace. A coderack subsystem is responsive to a slipnet network and is configured to instantiate a plurality of codelets in the codelet workspace each having one or more slots for binding with binding sites of lower level codelets and configured to monitor constraint changes thereto and propagate those changes to any related codelets. A temperature subsystem is configured to detect when temperature in the codelet workspace has reached a predetermined level indicating a satisfactory solution to the planning problem has been achieved.

Abstract: Methods, systems and computer program products for resolving multiple magnitudes assigned to a target vector are disclosed. A target vector that includes one or more target vector dimensions is received. One of the target vector dimensions is processed to determine a total number of magnitudes assigned to the processed target vector dimension. Also, a source vector that includes one or more source vector dimensions is received. The received source vector is processed to determine a total number of features associated with the source vector. When it is detected that the total number of magnitudes assigned to the processed target vector dimension exceeds one, one of the assigned magnitudes is selected based on one of the determined features associated with the source vector.

Abstract: An attribute estimation system and a method in which there are no cases that the estimation accuracy declines in a specific numerical value area, and an age estimation system, a gender estimation system and an age and gender estimation system using this is provided. It is a system to estimate an age of a person photographed in an input image, the system including: a classifier 3 that estimates the age of a person as a discrete quantity based on data of an input image; a classifier 4 that estimates the age of a person as a continuous quantity based on data of an input image; and an integration unit 7 that integrates an estimated result of the classifier 3 and an estimated result of the classifier 4.

Abstract: A system for managing a knowledge model defining a plurality of entities is provided. The system includes an extraction tool for extracting data items from disparate data sources that determines if the data item has been previously integrated into the knowledge model. The system also includes an integration tool for integrating the data item into the knowledge model that integrates the data item into the knowledge model only if the data item has not been previously integrated into the knowledge model. Additionally, a relationship tool for identifying, automatically, a plurality of relationships between the plurality of entities may also be provided. The system may also include a data visualization tool for presenting the plurality of entities and the plurality of relationships.

Abstract: In one embodiment, a method for forming a recognition algorithm for laser-induced breakdown spectroscopy may include: determining a most mathematically different dataset of a plurality of spectral datasets corresponding to materials; dividing the spectral datasets into model development datasets and performance evaluation datasets; transforming, automatically with a processor, one of the model development datasets into a first discrimination model that discriminates the first spectra; removing the first spectra from the model development datasets to yield a subset of development datasets; determining a next most mathematically different spectral dataset of the spectral datasets; transforming the subset of development datasets into a second discrimination model that discriminates the second spectra; and combining the first discrimination model and the second discrimination model to form the recognition algorithm for laser-induced breakdown spectroscopy.

Abstract: Implementations of the present disclosure include methods for managing data quality for event data. In some implementations, methods include receiving, at one or more computing devices, a plurality of events, each event comprising event data and being generated by an event source in response to a real-world activity, processing, using the one or more computing devices, the plurality of events using one or more complex event processing (CEP) rules to generate a complex event, in response to generating the complex event, annotating the complex event with quality information corresponding to each of the plurality of events to provide a quality-enriched event, and publishing the quality-enriched event.

Abstract: Data structures and apparatuses to represent knowledge are disclosed. The processes can comprise labeling elements in a knowledge signature according to concepts in an ontology and populating the elements with confidence values. The data structures can comprise knowledge signatures stored on computer-readable media. The knowledge signatures comprise a matrix structure having elements labeled according to concepts in an ontology, wherein the value of the element represents a confidence that the concept is present in an information space. The apparatus can comprise a knowledge representation unit having at least one ontology stored on a computer-readable medium, at least one data-receiving device, and a processor configured to generate knowledge signatures by comparing datasets obtained by the data-receiving devices to the ontologies.

Abstract: Logic circuits provide networks to simulate the functions of neural networks of the brain, and can discriminate degrees of state, and combinations of degrees of state, corresponding to a number of neurons. Logic circuits comprise Recursive AND NOT Conjunctions (RANCs), or AND NOT gates. A RANC is a general logic circuit that performs conjunctions for 2n possible combinations of truth values of n propositions. The RANCs function dynamically, with capabilities of excitation and inhibition. Networks of RANCs are capable of subserving a variety of brain functions, including creative and analytical thought processes. A complete n-RANC produces all conjunctions corresponding to the 2n possible combinations of truth values of n propositions.

Abstract: A method and system for predicting a next navigation event are described. Aspects of the disclosure minimize the delay between a navigation event and a network response by predicting the next navigation event. The system and method may then prerender content associated with the next navigation event. For example, the method and system may predict a likely next uniform resource locator during web browsing to preemptively request content from the network before the user selects the corresponding link on a web page. The methods describe a variety of manners of predicting the next navigation event, including examining individual and aggregate historical data, text entry prediction, and cursor input monitoring.

Abstract: Systems, devices, and methods for using Multi-Pattern Viterbi Algorithm for joint decoding of multiple patterns are disclosed. An exemplary method may receive a plurality of sets of time-sequential signal observations for each of a number K of signal repetitions. Further, each set of signal observations is associated with a respective dimension of a K-dimensional time grid having time-indexed points. Moreover, at each of a plurality of the time-indexed points, a state cost metric is calculated with a processor for each state in a set of states of a hidden Markov model (HMM). In addition, each state in the set of states and for a given time-indexed point, the state cost metric calculation provides a most-likely predecessor state and a corresponding most-likely predecessor time-indexed point. The exemplary method may also determine a sequence of states using the calculated state cost metrics and determine a corresponding cumulative probability measure for the HMM.

Abstract: Simulated neural circuitry is trained using sequences of images representing moving objects, such that the simulated neural circuitry recognizes objects by having the presence of lower level object features that occurred in temporal sequence in the images representing moving objects trigger the activation of higher level object representations. Thereafter, an image of an object that includes lower level object features is received, the trained simulated neural circuitry activates a higher level representation of the object in response to the lower level object features from the image, and the object is recognized using the trained simulated neural circuitry.