Abstract: A method, a system and an arrangement for predicting at least one system event and a corresponding computer program and a corresponding computer-readable storage medium are configured so that it is possible to predict a system event based on trends in observables over a certain period of time prior to the event occurring. One example of a system event is the failure of a system because the abnormal behavior of a component is reflected in irregularities in one or a plurality of observables. Another example of a system event is the early recognition or pre-acute prediction of a specific critical condition of a patient.

Abstract: A system mounted in a vehicle for classifying light sources. The system includes a lens and a spatial image sensor. The lens is adapted to provide an image of a light source on the spatial image sensor. A diffraction grating is disposed between the lens and the light source. The diffraction grating is adapted for providing a spectrum. A processor is configured for classifying the light source as belonging to a class selected from a plurality of classes of light sources expected to be found in the vicinity of the vehicle, wherein the spectrum is used for the classifying of the light source. Both the image and the spectrum may be used for classifying the light source or the spectrum is used for classifying the light source and the image is used for another driver assistance application.

Abstract: A method for making a decision includes receiving a first decision question from a first user, providing a database of information regarding elements of decision quality, populating the database with data corresponding to each element of decision quality as applied to the first decision question, and providing a decision recommendation for the first decision question based at least in part on the data.

Abstract: A model optimization system is configured to determine quality of variables for model generation. A data storage stores input variables, quality metrics for the input variables, and weights for the quality metrics. The quality metrics describe sufficiency of data for the input variables and the data is provided for a plurality of regions. A scoring module determines a score for each region based on the input variables and the weighted quality metrics. An optimizer determines whether at least one of the input variables for a region is to be modified based on the scores, and determines whether the total score for the region is operable to be improved using a modified input variable.

Abstract: Techniques of creating a classifier model for a multi-class linear classifier are disclosed. The classifier model includes feature weights for each of a plurality of feature and label combinations. One technique includes selecting an update set of feature weights to be updated from the classifier model, determining an update for each of the feature weights in the selected update set using a processor, a plurality of the updated determined independently of all other updates and determined based on a largest reduction in an output of a loss function, modifying each of the updates using a step size, and updating the classifier model feature weights using the modified, determined updates.

Abstract: A system is disclosed for obtaining and aggregating opinions generated by multiple sources with respect to one or more objects. The disclosed system uses observed variables associated with an opinion and a probabilistic model to estimate latent properties of that opinion. With those latent properties, the disclosed system may enable publishers to reliably and comprehensively present object information to interested users.

Abstract: The claimed subject matter provides systems and/or methods for training feature weights in a statistical machine translation model. The system can include components that obtain lists of translation hypotheses and associated feature values, set a current point in the multidimensional feature weight space to an initial value, chooses a line in the feature weight space that passes through the current point, and resets the current point to optimize the feature weights with respect to the line. The system can further include components that set the current point to be a best point attained, reduce the list of translation hypotheses based on a determination that a particular hypothesis has never been touched in optimizing the feature weights from at least one of an initial staring point or a randomly selected restarting point, and output the point ascertained to be the best point in the feature weight space.

Abstract: A behavioral security analysis system comprises a computational semantic parser configured to process data associated with a security information and event management (SIEM) system to generate a plurality of logical descriptors, and a learning engine coupled to the computational semantic parser and configured to generate a plurality of behavioral security descriptors based at least in part on at least a subset of the logical descriptors. The behavioral security descriptors are made accessible to an alerting engine of the SIEM system and utilized to generate one or more security alerts.

Abstract: Techniques for controlling motions using motion recognizers generated in advance by users are described. According to embodiment, the motion recognizers created by end users are utilized to control virtual objects displayed in a virtual environment. By manipulating one or more motion sensitive devices, end users could command what the objects to do in the virtual environment. Motion signals from each of the motion sensitive devices are recognized in accordance with the motion recognizers created in advance by the users. One or more of the motion signals are at the same time utilized to tune the motion recognizers or create additional motion recognizers. As a result, the motion recognizers are constantly updated to be more accommodating to the user(s).

Abstract: A method and system for supporting a clinical diagnosis is provided, the method including the steps of representing a patient by a plurality of initial symptoms, the symptoms including characteristics of the patient; determining, by querying and reasoning in a disease-symptom knowledge model, at least one likely disease for each of the plurality of initial symptoms; mapping the plurality of initial symptoms onto a set of said likely diseases; determining for each disease of said set of likely diseases a plurality of associated symptoms, the associated symptoms being associated to a respective disease of said set of likely diseases in said disease-symptom knowledge model; categorizing each of the plurality of associated symptoms into disjoint categories by mapping associated symptoms with initial symptoms; and representing likely diseases in the context of said categories of associated symptoms.

Abstract: A method, system and computer readable medium that generates a dialog model for use in automated dialog is disclosed. The method may include collecting a plurality of task-oriented dialog interactions between users and human agents for a given domain, identifying one or more task in each dialog interaction, identifying one or more subtasks in each identified task and associating relations between the subtasks, identifying a dialog act and a set of predicate-argument relations for each subtask, generating one or more clauses from the set of predicate-argument relations, storing the tasks, subtasks, dialog acts predicate-argument relations, and clauses from each dialog interaction as a dialog interaction set, generating a dialog management model using the stored dialog interaction sets.

Abstract: A system for prospectively identifying media characteristics for inclusion in media content is disclosed. A neural network database including media characteristic information and feature information may associate relationships among the media characteristic information and feature information. Personal characteristic information associated with target media consumers may be used to select a subset of the neural network database. A first set of nodes, representing selected feature information, may be activated. The node interactions may be calculated to detect the activation of a second set of nodes, the second set of nodes representing media characteristic information. Generally, a node is activated when an activation value of the node exceeds a threshold value. Media characteristic information may be identified for inclusion in media content based on the second set of nodes.

Abstract: In one embodiment, the present disclosure is a method and apparatus for classifying applications using the collective properties of network traffic. In one embodiment, a method for classifying traffic in a communication network includes receiving a traffic activity graph, the traffic activity graph comprising a plurality of nodes interconnected by a plurality of edges, where each of the nodes represents an endpoint associated with the communication network and each of the edges represents traffic between a corresponding pair of the nodes, generating an initial set of inferences as to an application class associated with each of the edges, based on at least one measured statistic related to at least one traffic flow in the communication network, and refining the initial set of inferences based on a spatial distribution of the traffic flows, to produce a final traffic activity graph.

Abstract: A system and method for solving a quadratic programming optimization problem with bound constraints using a semi-explicit QP solver with respect to an embedded platform is presented. A linear system of equations associated with a matrix (e.g., a Karush-Kuhn-Tucker matrix, KKT system) can be solved at each iteration of the solver based on a factorization approach. A set of partial factors with respect to the QP problem can be pre-computed off-line and stored into a memory. The factorization process of the KKT matrix can then be finished on-line in each iteration of the semi-explicit QP solver in order to effectively solve the QP optimization problems. The QP problem can be solved utilizing a standard active-set approach and/or a partial explicit approach based on a processor utilization and memory usage.

Abstract: Method of selectively predicting hydrocarbon concentration in a sample of unknown hydrocarbon concentration, by (i) separately subjecting two or more samples of known hydrocarbon concentration to infrared (IR) radiation, (ii) separately detecting an IR signal from the samples of known hydrocarbon concentration, (iii) analyzing the IR signals using a multivariate chemometric technique to produce a training data set, (iv) generating a predictive model for hydrocarbon concentration based on the training data set, (v) subjecting the unknown sample to infrared (IR) radiation, (vi) detecting an IR signal from the unknown sample, (vii) applying the predictive model to the IR signal from the unknown sample. Thereafter, as step (viii), hydrocarbon concentration in the unknown sample is selectively predicted.

Abstract: Systems and techniques are disclosed for determining an intervention for a patient based at least in part on a likelihood of the patient to adhere to a prescription. In some implementations, a patient profile for a patient is obtained from one or more computer-readable storage devices in a patient population, the patient profile including multiple patient attributes; an adherence score is obtained for the patient profile, the adherence score for predicting patient adherence based on one or more of the multiple patient attributes wherein the adherence score indicates a likelihood of adherence of the patient to a prescribed treatment; and the adherence score obtained for the patient profile is modified into a modified score for intervention based on a set of weights for weighting the patient attributes.

Abstract: Predicting gas saturation of a formation using neural networks. At least some of the illustrative embodiments include obtaining a gamma count rate decay curve one each for a plurality of gamma detectors of a nuclear logging tool (the gamma count rate decay curves recorded at a particular borehole depth), applying at least a portion of each gamma count rate decay curve to input nodes of a neural network, predicting a value indicative of gas saturation of a formation (the predicting by the neural network in the absence of a formation porosity value supplied to the neural network), and producing a plot of the value indicative of gas saturation of the formation as a function of borehole depth.

Abstract: A display device to display a representation of a set of states, each one of the states being associated with a corresponding variable. Each of the states displayed is designated as being a most significant state for the corresponding variable. Input representing a probability estimate to be associated with a displayed state is entered and used to generate data for use in an expert system.

Abstract: A content evaluation apparatus and a content evaluation method that can distribute contents that a user potentially needs without any omissions. A history class separation unit generates a separation plane for separating in a separation plane a characteristic vector of a selected content and characteristic vectors of non-selected contents stored in a browsing history table. A user characteristic vector calculation unit generates a user characteristic vector based on an orthogonal vector perpendicular to the separation plane thus generated and stores the user characteristic vector in a user characteristic vector management table. Subsequently, when a recommendation request receiving unit receives a recommendation request, a content evaluation unit evaluates the contents based on the stored user characteristic vector.

Abstract: A system includes a centralized repository for tracking rule content and managing subscriptions to rule content by organizations and providers utilizing the system; a rule-evaluation server for receiving requests for rule-evaluations for specific patients, wherein the server determines content needing to be evaluated and retrieves the content to be used; a rule engine for performing the evaluations, wherein content, patient data, and rule evaluation parameters are provided to the engine, and the engine returns recommendations triggered by the evaluation, if any; an aggregator for aggregating recommendations from multiple sources, detecting and coordinating related recommendations, and applying configuration settings based on the patient and/or provider in context; and a client component for coordinating communication between an electronic health records system, the server, and the aggregator.