Abstract: Systems and methods for defining a custom segment in a set of behavioral data are provided. A described method includes receiving a set of behavioral data associated with a plurality of user devices and identifying multiple cohort groups, each of the cohort groups including one or more of the user devices. The behavioral data includes a behavior metric for each of the user devices and the cohort groups are identified based on the behavior metric for each of the user devices. The method further comprises generating a segmentation interface including a graphical visualization of the multiple cohort groups and causing the segmentation interface to be presented via a user interface device. The method further comprises defining a custom segment of the behavioral data based on a user selection of one or more of the multiple cohort groups via the segmentation interface.

Abstract: According to one embodiment of the disclosure, a graphical model-driven system includes a graphical knowledge pattern system coupled to a modeler interface. The graphical knowledge pattern system has a graphical knowledge pattern library for the storage of a plurality of graphical knowledge patterns that are configured to classify information according to one or more information related criteria. The modeler interface is operable to receive a request for information from the user interface and retrieve information from one or more knowledge based systems according to one or more information related criteria of a particular graphical knowledge pattern.

Abstract: A control parameters adjustment method of an air conditioning system has the following steps: (a) building up a comparison table; (b) determining an objective output power; (c) determining whether there is only one preferred operation point by searching the comparison table; (d) when there is only one preferred operation point, adjusting the control parameters based on the preferred operation point; (e) when there are a plurality of preferred operation points, executing a retrieval procedure for selecting one of the preferred operation points, and adjusting the control parameters based on the selected preferred operation point. The method adjusts the control parameters according to the comparison table for ensuring that the air conditioning system can be operated with the minimum power consumption.

Abstract: The present inventions generally relate to a leaf node ranking method in decision trees for spatial prediction and its recording medium. The leaf node ranking method in decision trees includes a learning step to form a decision tree having one root node, in which each parent node has multiple child nodes, using training data sets for spatial prediction; and a leaf node ranking step from the decision tree that finishes the learning. In the learning step, each node of the decision tree stores both the number of classes according to class distribution of training data and structures for storing the number. In the leaf node ranking step, a rank of a leaf node is determined using the number of classes according to class distribution, which is stored in each node on a path from the root node to the leaf node.

Abstract: Described is a high-dimensional optimization system implementing a modification of particle swarm optimization called self-aware particle swarm optimization. A plurality of software agents is configured to operate as a cooperative swarm to locate an objective function optima in a multi-dimensional solution space. The plurality of software agents is influenced by a set of parameters which influence exploration of the multi-dimensional solution space and convergence on the objective function optima. The plurality of software agents automatically modifies the set of parameters in response to at least one measure of convergence progress. Self-aware particle swarm optimization allows for monitoring of simple convergence properties to provide feedback to the swarm dynamics and make the swarm self-aware and adjust itself to the problem being solved.

Abstract: A system and method for searching a finite collection of images using at least one semantic network. Upon receipt of a query from a user that includes a theme and one or more initial keywords, a set of keywords based on the theme and including the initial keywords is generated from one or more semantic networks corresponding to the theme and/or initial keywords. When the finite collection of images includes suitable metadata, a result set is generated of images corresponding to the expanded set of keywords. When the finite collection includes images lacking in metadata, a remote third-party image collection is searched with the set of keywords to obtain a result set that is used to train visual classifiers as to visual concepts associated with the keywords. The classifiers are used to classify the images in the finite collection lacking metadata and the search of the finite collection is performed with the set of keywords to generate a result set.

Abstract: Systems and methods for efficiently detecting and coordinating step changes, trends, cycles, and bursts affecting lexical items within data streams are provided. Data streams can be sourced from documents that can optionally be labeled with metadata. Changes can be grouped across lexical and/or metavalue vocabularies to summarize the changes that are synchronous in time. The methods described herein can be applied either retrospectively to a corpus of data or in a streaming mode.

Abstract: A visual representation of a human user for display within one or more graphical user interfaces to others interacting with the human user over a network can be identified. An authenticity score defining a degree of resemblance between the visual representation of the human user and physical characteristics of the human user can be calculated.

Abstract: A method and associated systems for automatically generating an ontology and a set of axioms from a business-process model that represents the operations of a business. This ontology and set of axioms may be used to create the knowledgebase of an artificially intelligent expert system that emulates the business operations. A processor parses a representation of business processes stored in the business-process model, deriving a set of axioms and a set of entity classes from the parsed data. The processor uses these axioms and classes to identify concept nodes and process nodes, which it organizes into the ontology of the knowledgebase. The processor further identifies information derived from the parsed data to create a set of triple data items, each of which represents the information represented by one or more of the derived axioms. These triples are stored in the knowledgebase as a triple store data structure.

Abstract: A method and system comprise providing means and method for producing, modifying, and/or exploiting the structure of a policy manifold. Each of the policies at least comprises information for mapping state and/or sensory information as input to action preferences as output. One or more processing units assign each of the policies a policy coordinate on a policy manifold. The policy coordinate may in part be determined by a dissimilarity matrix or other means for organizing the coordinates of the policies on the policy manifold according to the properties of the policies and the topology of the policy manifold. The policy manifold comprises a dimensionality that is lower than a combined dimensionality of the input and the output, wherein the policy manifold at least in part determines a behavior of the intelligent artificial agent.

Abstract: The present invention is a method and an apparatus for reward-based learning of policies for managing or controlling a system or plant. In one embodiment, a method for reward-based learning includes receiving a set of one or more exemplars, where at least two of the exemplars comprise a (state, action) pair for a system, and at least one of the exemplars includes an immediate reward responsive to a (state, action) pair. A distance metric and a distance-based function approximator estimating long-range expected value are then initialized, where the distance metric computes a distance between two (state, action) pairs, and the distance metric and function approximator are adjusted such that a Bellman error measure of the function approximator on the set of exemplars is minimized. A management policy is then derived based on the trained distance metric and function approximator.

Abstract: Disclosed are various embodiments for predictive network page loading. Content corresponding to a network request is obtained. A next network page associated with the obtained page content is predicted. If the prediction is confident relative to a predetermined confidence threshold, then a first network page is generated. The first network page includes the page content corresponding to the network request and a portion of content for the predicted network page. The generated first network page is provided to a client. A request for another network page is received. If the another network page in the request corresponds to the predicted next network page, a second network page is generated. The second network page includes the remainder of the content for the predicted page content.

Abstract: A truth maintenance method and system. The method includes receiving by a computer processor, health event data associated with heath care records for patients. The computer processor associates portions of the health event data with associated patients and related records in a truth maintenance system database. The computer processor derives first health related assumption data and retrieves previous health related assumption data derived from and associated with previous portions of previous health event data. The computer processor executes non monotonic logic with respect to the first health related assumption data and the previous health related assumption data. In response, the computer processor generates and stores updated first updated health related assumption data associated with the first health related assumption data and the previous health related assumption data.

Abstract: An authentication system authenticates an object. The authentication system includes a capture device for capturing at least one biometric output data record (BD) for the object; a reading device for reading configuration data (Konf), associated with the object, for an artificial neural network; a processing device designed to produce the artificial neural network and to input the BD into the neural network; a verification device which captures an output from the neural network to authenticate the object, wherein the neural network is a bidirectional associative memory, particularly a Hopfield network, having a multiplicity of network states. The verification device is designed to determine the output from the neural network by capturing a final state derived from the input of the BD. The neural network stores a key associated with a particular person. The key is released only when appropriate biometric data are input into the neural network.

Abstract: Techniques for modifying search results associated with a search request based on a determination that a member profile attribute is inaccurate are described. According to various embodiments, an existing member profile attribute included in a member profile page of a particular member of an online social network service is identified. Member profile data and behavioral log data associated with a plurality of members of the online social network service is then accessed. Prediction modeling to verify the existing member profile attribute is performed using the accessed data. Additionally, a confidence score associated with the existing member profile attribute is generated based on the prediction modeling. Moreover, the existing member profile attribute is determined to be inaccurate based on the generated confidence score. Furthermore, the search results associated with a search request is modified based on the determination.

Abstract: A computer-implemented system includes an edge module and at least one input device coupled to the edge module. The at least one input device is configured to generate data input signals. The system also includes a cognitive module coupled to the edge module. The cognitive module includes a perception sub-module coupled to the edge module. The perception sub-module is configured to receive the data input signals. The cognitive module also includes a learning sub-module coupled to the perception sub-module. The learning sub-module is configured to adaptively learn at least in part utilizing the data input signals.

Abstract: A query device scans radio frequencies for visible transmitting devices. The querying device receives at least a signal strength and identifier information associated with each of the transmitting devices. The list of visible devices is used to query a database containing location information for a plurality of visible devices. The list may be sent to a locationing system that may perform a location analysis on the resulting data to return a location to the query device. The weighted average of the locations returned in the database query may be computed to determine the location of the querying device, with the weight for each of the locations being the current signal strength detected by the querying device. Neural network analysis may also be used to determine the location of the querying device. Learning and seeding operations many also be used to populate the database with location information for transmitting devices.

Abstract: Systems and methods are disclosed for using machine learning (e.g., neural networks and/or combinatorial learning) to solve the non-linear problem of predicting the provisioning of a server farm (e.g., cloud resources). The machine learning may be performed using commercially available products, such as the SNNS product from The University of Stuttgard of Germany. The system, which includes a neural network for machine learning, is provided with an identification of inputs and outputs to track, and the system provides correlations between those. Rather than static rules, the machine learning provides dynamic provisioning recommendations with corresponding confidence scores. Based on the data collected/measured by the neural network, the provisioning recommendations will change as well as the confidence scores.

Abstract: A system, method and computer program product for automatic document classification, including an extraction module configured to extract structural, syntactical and/or semantic information from a document and normalize the extracted information; a machine learning module configured to generate a model representation for automatic document classification based on feature vectors built from the normalized and extracted semantic information for supervised and/or unsupervised clustering or machine learning; and a classification module configured to select a non-classified document from a document collection, and via the extraction module extract normalized structural, syntactical and/or semantic information from the selected document, and generate via the machine learning module a model representation of the selected document based on feature vectors, and match the model representation of the selected document against the machine learning model representation to generate a document category, and/or classificatio

Abstract: Certain aspects of the present disclosure support a technique for adaptive bit-allocation in neural systems. Bit-allocation for neural signals and parameters in a neural network described in the present disclosure may comprise for a plurality of synapse circuits in the neural simulator network, dynamically allocating a number of bits to the neural circuit signals based on at least one characteristic of one or more neural potential in the neural simulator network; and for the plurality of synapse circuits in the neural simulator network, dynamically allocating a number of bits to at least one neural processing parameter of the synapse circuit based on at least one condition of the neural simulator network.