Abstract: Contextual graph matching based anomaly detection may include evaluating computer-generated log file data to create a master directed graph that specifies known events and transitions between the known events. The master directed graph may be processed to determine a plurality of decomposed master graph walks. Incoming computer-generated log file data may be evaluated to create an incoming directed graph that specifies unknown events and transitions between the unknown events. The incoming directed graph may be processed to determine a decomposed incoming walk. Overlap, distance difference, and correlation scores may be determined for each walk pair of a plurality of walk pairs including each of the plurality of decomposed master graph walks and the decomposed incoming walk. One of the decomposed master graph walks may be selected based on the overlap score, the difference score, and the correlation score, to detect an anomaly.

Abstract: Methods and system for modeling the behavior of binary synapses are provided. In one aspect, a method of modeling synaptic behavior includes receiving an analog input signal and transforming the analog input signal into an N-bit codeword, wherein each bit of the N-bit codeword is represented by an electronic pulse. The method includes loading the N-bit codeword into a circular shift register and sending each bit of the N-bit codeword through one of N switches. Each switch applies a corresponding weight to the bit to produce a weighted bit. A signal corresponding to a summation of the weighted bits is output and represents a synaptic transfer function characterization of a binary synapse.

Abstract: The present invention provides an attribute value estimation device capable of yielding highly accurate estimation results even when people from multiple races are estimation targets. The attribute value estimation device for estimating, from data input thereto, an attribute value of the data includes: a data acquisition unit (1) that acquires data for which an attribute value is to be estimated; a discrete quantity estimation unit (2) that estimates the attribute value as a discrete quantity based on the data acquired by the data acquisition unit (1) and in accordance with a previously learned determination criterion; a first LSPC (3) that estimates the attribute value as a discrete quantity based on data input from the discrete quantity estimation unit (2); and an integration unit (4) that integrates a first discrete quantity estimation value estimated by the discrete quantity estimation unit (2) and a second discrete quantity estimation value estimated by the first LSPC (3).

Abstract: A method and system for geolocation. The method and system determines the geographic history of a target of interest (i.e., the probability that a sample of interest passed though a defined location) given a combination of spatially attributable input data and weighing distributions, and modifying the probability for each possible location using the equation P ? ( x ) = ? k K ? ( w ? ( k ) · n x ) ? k K ? ( w ? ( k ) · n t ) , where P(x) is the probability that the sample of interest passes through a given geographic location X, nt is the total number of possible legal travel routes for the sample of interest, nx is the number of legal travel routes including location X, k is the travel route's cardinality, and w(k) is the assigned weight for cardinality k from the user-defined weighting function.

Abstract: Apparatus and methods for an extensible robotic device with artificial intelligence and receptive to training controls. In one implementation, a modular robotic system that allows a user to fully select the architecture and capability set of their robotic device is disclosed. The user may add/remove modules as their respective functions are required/obviated. In addition, the artificial intelligence is based on a neuronal network (e.g., spiking neural network), and a behavioral control structure that allows a user to train a robotic device in manner conceptually similar to the mode in which one goes about training a domesticated animal such as a dog or cat (e.g., a positive/negative feedback training paradigm) is used. The trainable behavior control structure is based on the artificial neural network, which simulates the neural/synaptic activity of the brain of a living organism.

Abstract: A system and method for generating personalized channels of multimedia content are presented. The method comprises receiving a captured multimedia content from a user; generating a signature based on the captured multimedia content; searching for additional multimedia content that match the captured multimedia content based on the generated signature and at least one compared signature, wherein each compared signature of the at least one compared signature is associated with an additional multimedia content; and generating a personalized channel to be displayed on a web-browser of a user device, wherein the personalized channel comprises the captured multimedia content and at least one multimedia content of the additional multimedia content.

Abstract: The present invention provides an event-driven universal neural network circuit. The circuit comprises a plurality of neural modules. Each neural module comprises multiple digital neurons such that each neuron in a neural module has a corresponding neuron in another neural module. An interconnection network comprising a plurality of digital synapses interconnects the neural modules. Each synapse interconnects a first neural module to a second neural module by interconnecting a neuron in the first neural module to a corresponding neuron in the second neural module. Corresponding neurons in the first neural module and the second neural module communicate via the synapses. Each synapse comprises a learning rule associating a neuron in the first neural module with a corresponding neuron in the second neural module. A control module generates signals which define a set of time steps for event-driven operation of the neurons and event communication via the interconnection network.

Abstract: Technologies are generally provided for maintaining performance level of a database being migrated between different cloud-based service providers employing machine learning. In some examples, data requests submitted to an original data store/database may be submitted to a machine learning-based filter for recording and analysis. Based on the results of the data requests and the filter analyses, new key value structures for a new data store/database may be created. The filter may assign performance scores to the original data requests (made to the original data store) and data requests made to the newly-created key value structures. The filter may then compare the performance scores associated with the created key value structures to each other and to performance scores associated with the original data requests and may select the created key value structures with performance scores that are at least substantially equal to those of the original data requests for the new data store.

Abstract: Roughly described, a computer-implemented evolutionary data mining system includes a memory storing a candidate gene database containing active and shadow individuals; a gene pool processor which tests only active individuals on training data and updates their fitness estimates; a competition module which selects individuals (both active and shadow) for discarding from the gene pool in dependence upon both their updated fitness estimate and their testing experience level; and a gene harvesting module providing for deployment selected ones of the individuals from the gene pool. The gene database has an experience layered elitist pool, and individuals to compete only with other individuals in their same layer. Shadow individuals are created in each layer for active individuals that survive all competition with the layer before their testing experience exceeds the testing experience range for the layer.

Abstract: Methods, systems and computer program products are disclosed for detecting patterns in a data stream that match multi-pattern rules. One embodiment of the invention provides a method of recognizing a specified group of patterns in a data stream. The method comprises identifying a rule for said specified group of patterns in the data stream, and using a first array of finite state machines to scan the data stream for at least some of the patterns in the specified group. For patterns in the specified group that are found in the data stream by the first array of finite state machines, pattern identifiers are sent to a second array of finite state machines. The second array of finite state machines determines if the specified group of patterns is in the data stream in accordance with the identified rule by, at least in part, using said pattern identifiers.

Abstract: A method and apparatus for identifying deformation of a structure. Training deformation data is identified for each training case in a plurality of training cases. Training strain data is identified for each training case in the plurality of training cases. The training deformation data and the training strain data are configured for use by a heuristic model to increase an accuracy of output data generated by the heuristic model. A group of parameters for the heuristic model is adjusted using the training deformation data and the training strain data for the each training case in the plurality of training cases such that the heuristic model is trained to generate estimated deformation data for the structure based on input strain data. The estimated deformation data has a desired level of accuracy.

Abstract: A system and method for analysis of complex systems which includes determining model parameters based on time series data, further including profiling a plurality of types of data properties to discover complex data properties and dependencies; classifying the data dependencies into predetermined categories for analysis; and generating a plurality of models based on the discovered properties and dependencies. The system and method may analyze, using a processor, the generated models based on a fitness score determined for each model to generate a status report for each model; integrate the status reports for each model to determine an anomaly score for the generated models; and generate an alarm when the anomaly score exceeds a predefined threshold.

Abstract: An information classification system includes a server including a knowledge base that receives classification information to be classified, conducts language analysis of the classification information to acquire and classify a plurality of keywords into elements made up of a classification target word and a related word that modifies the classification target word, and conducts a search with the related word, so as to assign a classification identification value to the information; a classification candidate extraction section that extracts the classification identification value that the knowledge base assigns to generate an automatic classification result; and a classification update section that receives the automatic classification result and corrects registered items in the knowledge base with a correction value received through a GUI for classification confirmation while referring to log data that is a processing history about automatic classification for the language analysis and the element classific

Abstract: A method for estimating artist ambiguity in a dataset is performed at a device with a processor and memory storing instructions for execution by the processor. The method includes applying a statistical classifier to a first dataset including a plurality of media items, wherein each media item is associated with one of a plurality of artist identifiers, each artist identifier identifies a real world artist, and the statistical classifier calculates a respective probability that each respective artist identifier is associated with media items from two or more different real world artists based on a respective feature vector corresponding to the respective artist identifier. The method further includes providing a report of the first dataset, including the calculated probabilities, to a user of the electronic device. Each respective feature vector includes a plurality of features that indicate likelihood of artist ambiguity.

Abstract: Systems and methods for reducing a churn rate associated with subscribers of social network services are described. In some example embodiments, the systems and methods may access activity information associated with a former subscriber of a social network service, compare the accessed activity information to activity information associated with subscribers of the social network service, identify one or more differences between the activity information associated with the former subscriber of the social network service and the activity information associated with the subscribers of the social network service, and perform an action based on the identified one or more differences.

Abstract: Adaptive controller apparatus of a plant may be implemented. The controller may comprise an encoder block and a control block. The encoder may utilize basis function kernel expansion technique to encode an arbitrary combination of inputs into spike output. The controller may comprise spiking neuron network operable according to reinforcement learning process. The network may receive the encoder output via a plurality of plastic connections. The process may be configured to adaptively modify connection weights in order to maximize process performance, associated with a target outcome. The relevant features of the input may be identified and used for enabling the controlled plant to achieve the target outcome. The stochasticity of the learning process may be modulated. Stochasticity may be increased during initial stage of learning in order to encourage exploration. During subsequent controller operation, stochasticity may be reduced to reduce energy use by the controller.

Abstract: A recommended tire selection system 100 comprises a driving condition obtaining unit 101 that obtains a driving condition of a vehicle on which the recommended tire is loaded, a retread necessity obtaining unit 103 that obtains the retread necessity to replace the tread; a performance decision unit 105 that decides a rolling resistance and a wear resistance of the recommended tire based on the obtained driving condition, and decides a casing durability based on the obtained retread necessity; and a selection unit 107 that selects a recommended tire that is a combination of a tread and a casing, fulfilling the decided rolling resistance, wear resistance, and casing durability.

Abstract: A rule input to generate a rule is received. The rule provides an action for an event. A content experience engine generates the rule for a set of content. The content experience engine receives an interaction input based upon the set of content from the content experience configuration. The content experience engine generates an enhanced set of content based upon the rule and the interaction input.

Abstract: An information technology (IT) architecture upper merged ontology which includes an upper merged ontology that is a representation framework for combined knowledge sources that are used in business and information technology; an upper merged ontology knowledge base containing information derived from an analysis of business and technical services pertaining to the IT architecture application; a plurality of semantic web application programming interfaces (APIs) that allow access to the upper merged ontology as a semantic web; a reasoning and constraint language (RaCL) that is a scripting language having the capabilities of model creation, automated reasoning and search and query operations such that scripts developed in the reasoning and constraint language can execute using the semantic web APIs; and a computer processor for representing and performing execution tasks.

Abstract: Apparatus and methods for an extensible robotic device with artificial intelligence and receptive to training controls. In one implementation, a modular robotic system that allows a user to fully select the architecture and capability set of their robotic device is disclosed. The user may add/remove modules as their respective functions are required/obviated. In addition, the artificial intelligence is based on a neuronal network (e.g., spiking neural network), and a behavioral control structure that allows a user to train a robotic device in manner conceptually similar to the mode in which one goes about training a domesticated animal such as a dog or cat (e.g., a positive/negative feedback training paradigm) is used. The trainable behavior control structure is based on the artificial neural network, which simulates the neural/synaptic activity of the brain of a living organism.