Abstract: Methods, systems, and articles of manufacture for annotating of an image are disclosed. These include scoring the image using a plurality of trained classifiers, wherein each of the trained classifiers corresponds to at least one of a plurality of image groups clustered based upon image similarity, and wherein each image group is associated with a set of weighted labels; selecting one or more of the image groups based upon the scoring; aggregating one or more sets of weighted labels associated with the selected one or more image groups; and annotating the image using the aggregated one or more sets of weighted labels.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a plurality of different types of predictive models using training data, wherein each of the predictive models implements a different machine learning technique. One or more weights are obtained wherein each weight is associated with an answer category in the plurality of examples. A weighted accuracy is calculated for each of the predictive models using the one or more weights.

Abstract: The disclosure is directed to systems and methods for estimating the completion time of a web-based survey. The estimated completion time can be determined based on survey parameters associated with the web-based survey. The survey parameters can be based on characteristic values of the web-based survey associated with the survey parameters. The characteristic values of survey parameters can be used to estimate parameter completion times, from which an estimated survey completion time for the web-based survey may be determined. The disclosed systems and methods further permit assessments of survey feasibility.

Abstract: Embodiments of the present invention are directed to reducing the computational cost of propagating the uncertainty in the state of an object, which requires the numerical solution of an initial value problem (IVP) ensemble, by means of a computer. The present invention uses an implicit-Runge-Kutta-based (IRK) method that exploits the proximity of the initial conditions within the IVP ensemble. More specifically, one of the IVPs can be solved over a given time span consisting of one or more time steps with an IRK method, and the resulting trajectory can be recorded. The system of equations that arises on each time step of the IRK method can be solved using various iterative methods. The remaining IVPs can be solved over the same timespan using the same IRK method and time steps, together with the recorded trajectory from the first propagation.

Abstract: Embodiments of the present invention characterizing the uncertainty of the orbital state of an Earth-orbiting space object hereof using a Gauss von Mises probability density function defined on the n+1 dimensional cylindrical manifold n×. Additionally, embodiments of the present invention can include transforming a Gauss von Mises distribution under a diffeomorphism and approximating the output as a Gauss von Mises distribution. Embodiments of the present invention can also include fusing a prior state represented by a Gauss von Mises distribution with an update report, wherein the update can be either another Gauss von Mises distribution of the same dimension as the prior or an observation related to the prior by a stochastic measurement model. A Gauss von Mises distribution can be calculated from a plurality of reports, wherein the reports are either Gauss von Mises distributions or observations related to the state space by a stochastic measurement model.

Abstract: One or more systems and methods for knowledge pattern search from networked agents are disclosed in various embodiments of the invention. A system and a related method can utilizes a knowledge pattern discovery process, which involves analyzing historical data, contextualizing, conceptualizing, clustering, and modeling of data to pattern and discover information of interest. This process may involve constructing a pattern-identifying model using a computer system by applying a context-concept-cluster (CCC) data analysis method, and visualizing that information using a computer system interface. In one embodiment of the invention, once the pattern-identifying model is constructed, the real-time data can be gathered using multiple learning agent devices, and then analyzed by the pattern-identifying model to identify various patterns for gains analysis and derivation of an anomalousness score.

Abstract: A software optimization system isolates an effect of a change in a control variable from effects of ongoing, unknown changes in other variables. The system discards effects due to noise so that effects of interest to a programmer are more easily visible. The software optimization system treats variations in one or more control variables and in the output of the system as signals. The system varies the control variable at a specific frequency unlikely to correlate with uncontrolled variations in external variables. The system uses digital signal processing (DSP) techniques to filter the output, isolating the frequency of the control variable variation. The system then compares the resulting filtered output to the input to determine the approximate effect of the variation in the control variable.

Abstract: The method for implementing meta rules on an executable rule engine includes parsing a business rule as input into a meta rule on a rule engine. The meta rule includes a first expression defining one or more style conditions for business rules. The business rule includes a second expression defining logic for an entity. The method includes determining that a business rule violates a style condition defined by the meta rule. The business rule and the meta rule conform to a common business rule syntax. The method includes generating a notification in response to determining that the business rule violates the style condition of the meta rule.

Abstract: Systems and methods for classifying a set of documents are provided. In some aspects, a method includes receiving a subset of the set of documents. The method also includes automatically determining at least one classification rule for the subset of documents based on the documents in the subset. At least a true-positive threshold proportion of documents in the subset of documents follows the at least one classification rule. At most a false-positive threshold proportion of documents in the set of documents and not in the subset of documents follow the at least one classification rule. The method also includes storing the at least one classification rule in association with the subset of documents.

Abstract: Disclosed herein is a computer implemented method and system for analyzing load responsive behavior of infrastructure components in an electronic environment for proactive management of the infrastructure components. Transaction data on multiple application transactions is collected. Load patterns are identified from the collected transaction data for generating load profiles. Data on infrastructure behavior in response to the application transactions is collected. Infrastructure behavior patterns are identified from the infrastructure behavior data for generating behavior profiles. The generated load profiles and the generated behavior profiles are correlated to create a load responsive behavior model. The created load responsive behavior model predicts behavior of the infrastructure components for different load patterns. A live data stream from current application transactions is analyzed using the load responsive behavior model to determine current load responsive behavior.

Abstract: Systems and methods directed toward processing optimization problems using loss functions, wherein a loss function is decomposed into at least one stratum loss function, a loss is decreased for each stratum loss function to a predefined stratum loss threshold individually using gradient descent, and the overall loss is decreased to a predefined threshold for the loss function by appropriately ordering the processing of the strata and spending appropriate processing time in each stratum. Other embodiments and aspects are also described herein.

Abstract: Systems and methods use machine learning techniques to resolve location ambiguity in search queries. In one aspect, a dataset generator generates a training dataset using query logs of a search engine. A training engine applies a machine learning technique to the training dataset to generate a location disambiguation model. A location disambiguation engine uses the location disambiguation model to resolve location ambiguity in subsequent search queries.

Abstract: An artificially intelligent or rule-based system to assist teams or groups become more effective by improving the communication process between members of the team or group. The system helps members share information, negotiate more effectively and make better group decisions. The system is designed to allow users to provide feedback to the system regarding undetected emotional feelings of any one user to all users of the system.

Abstract: An interactive tool is described for modifying the behavior of a system, such as, but not limited to, the behavior of a classification system. The tool uses an interface mechanism to present a current global state of the system. The tool accepts one or more refinements to this global state, e.g., by accepting individual changes to parameter settings that are presented by the interface mechanism. Based on this input, the tool computes and displays the global implications of the updated parameter settings. The process of iterating over one or more cycles of user updates, followed by computation and display of the implications of the attempted refinements, has the effect of advancing the system towards a global state that exhibits desirable behavior.

Abstract: Embodiments provide a method for maintaining digital assets of a user. The digital assets reside on at least one device and/or at least one cloud. The method further comprises aggregating the digital assets, and obtaining information identifying digital assets available for access by an access device. The digital assets available for access by the access device include a digital asset residing on a cloud or on a device other than the access device.

Abstract: Life cycle management of rule sets, each rule set including rules for managing the operation of a computing system, including: identifying, by a life cycle management module, a rule life cycle state for each rule in the rule set, wherein the rule life cycle state specifies the current deployment status of the rule; identifying, by the life cycle management module, a linkage set for each rule in the rule set, wherein the linkage set identifies versions of the rule that are in a different rule life cycle state; and updating, by the life cycle management module, the rule set, including: updating the rule life cycle state for one or more rules in the rule set; and updating the linkage set for one or more rules in the rule set.

Abstract: A system and method of predicting items likely to appeal to a user, based on data received from a remote site. One or more local systems are provided, along with a remote system, said local systems communicating with said remote system over a network connection. Each local system maintains a set of characterized items. A local processor predicts an appeal of an item to a user based on the comparison with the set of characterized items with, e.g., user preferences. The user may interact through graphical user interface. At least one user-preference predictive algorithm may be employed to present items predicted to appeal to the user.

Abstract: Methods and systems for forecasting a future event in an event stream are provided herein. The event stream includes a number of events, wherein each of the events includes an event label and an associated time stamp. The system includes a processor that is adapted to execute stored instructions and a system memory. The system memory includes code configured to generate one or more decision graphs for predicting events with event labels chosen from a number of event labels based on the events within the event stream, wherein the decision graph includes decision nodes and leaf nodes, and wherein each of the decision nodes includes a question. The system memory also includes code configured to analyze event stream data using the decision graph and forecast a future event in the event stream based on the analysis of the event stream data.

Abstract: There is provided a temporal processor node for use as an input node in the input layer of a class network in an artificial neural network, the class network being operable to generate an output signal based on a network input vector component received by the input layer, the temporal processor node being operable to receive observation data representing the observed state of a monitored entity as a component of the network input vector.

Abstract: A system and method for declarative network access control are provided. The system includes an interpreter, a rules engine, a storage device, and a processor. The interpreter transforms sentences in a declarative network access control language to rules. The rules engine evaluates the rules to produce actions for providing access control to a network at a point of access. The storage device stores instructions for the interpreter, the rules and the rules engine and the processor executes those instructions.