Abstract: An exemplary embodiment of the present invention provides a method of processing network activity data. The method includes receiving network activity data and generating an event based on the network activity data. The method also includes generating a probability based at least in part on Bayesian statistics, the probability corresponding to a likelihood that the event caused or was caused by another event. The method also includes generating an event message corresponding to the event based on the probability.

Abstract: A method, devices, computer program products and an internet service is disclosed for adapting a context model. In the method a media clip is received. Also sensor data captured at least partly when the media clip was captured is received. A context is derived using a context model based at least partly on the sensor data and an indication on the context is provided to a user by receiving a search query having at least one context-related search criterion, deriving a similarity between the context and the at least one context-related search criterion and causing at least a part of the media clip to be presented as a response to the search query. Feedback on the relevance of the context is received from the user. Based on the feedback, the context model may then be adapted.

Abstract: A computer implemented method for generating a set of updates for a database comprising multiple records including erroneous, missing and inconsistent values, the method comprising using a set of partitioning functions for subdividing the records of the database into multiple subsets of records, allocating respective ones of the records to at least one subset according to a predetermined criteria for mapping records to subsets, applying multiple machine learning models to each of the subsets to determine respective candidate replacement values representing a tuple repair for a record including a probability of candidate and current values for the record, computing probabilities to select replacement values for the record from among the candidate replacement values which maximise the probability for values of the record for an updated database.

Abstract: A social networking system receives messages from users that include hashtags. The social networking system may use a natural language model to identify terms in the hashtag corresponding to words or phrases of the hashtag. The words or phrases may be used to modify a string of the hashtag. The social networking system may also generate computer models to determine likely membership of a message with various hashtags. Prior to generating the computer models, the social networking system may filter certain hashtags from eligibility for computer modeling, particularly hashtags that are not frequently used or that more typically appear as normal text in a message instead of as a hashtag. The social networking system may also calibrate the computer model outputs by comparing a test message output with outputs of a calibration group that includes positive and negative examples with respect to the computer model output.

Abstract: The concepts relate to task completion and specifically to aiding a user to complete an unfinished task at a subsequent time and/or on another device. One example can identify that a user is working on a task on a computing device associated with the user. In an instance when the user stops using the computing device without completing the task, the example can predict a likelihood that the user will subsequently resume the task on a second computing device associated with the user. In an instance where the likelihood exceeds a threshold, the example can attempt to aid the user in completing the task on the second computing device.

Abstract: Methods and systems to learn an optimal user experience. The system receives a request over a network from a user. The request includes context information. The system identifies a response to the request is to be utilized to learn whether a first interface component included in a first plurality of interface components is an optimal choice for a first decision. The response includes an interface. The interface includes the first interface component. The system identifies the response to the request is to be utilized based on the context information. Finally, the system communicates the response over the network to the user.

Abstract: A system (10) testing and rating operation-dependent processes and/or components (20) in automated production and test sequences comprises a robot (12) which by means of a minimum of one sensor (14, 16) detects test/measured values (M) of at least one operating and/or display element (22, 24) of the component (20) to be tested respectively rated and transmits to an analyzer (40) analyzing and rating the measured values (M) by means of defined quality functions (50), said quality functions by means of operators (52) imitating human rating schematics respectively rules and based on this processing result generating at least one rating.

Abstract: An information processing apparatus includes: strength estimation means for calculating an operation frequency for each predetermined time period from an operation history, and estimating a time period of the operation frequency which exceeds a predetermined value as a time period over which the user has a strong desire to adjust; detecting means for calculating, from the operation history, an operation frequency for each function of the electronic apparatus of the operation history in a time period corresponding to the time period of the strong desire to adjust estimated by the strength estimation means, and detecting a feature of the adjustment operation made by the user for the function of the electronic apparatus, the feature corresponding to the operation frequency for each function of the electronic apparatus; and determination means for determining a function, desired by the user to adjust, of the electronic apparatus, the function corresponding to the feature detected by the detecting means.

Abstract: The methods disclosed comprise the linehaul optimization algorithm which is a hybrid algorithm incorporating the basic structure of the Hungarian Assignment algorithm or equivalent and an improvement heuristic. However, in this particular case there is not a direct application of the existing, well-known Hungarian Assignment algorithm or equivalent in conjunction with a heuristic. The structure of the assignment model being used here is based on the Hungarian Assignment algorithm or equivalent but has been revised and enhanced to identify systematically (by using grouping) a number of equivalent optimal solutions (that give the same optimal, minimum cost for a particular iteration). A heuristic is then used to identify the ‘best’ optimal solution, of the many identified, that would contribute the most incremental cost reduction in future iterations of the heuristic.

Abstract: Disclosed are methods, systems, and computer-readable media for determining stability of a neural system. The method includes tracking a function world line of an N element neural system within at least one behavioral space, determining whether the tracking function world line is approaching a psychological stability surface, and implementing a quantitative solution that corrects instability if the tracked function world line is approaching the psychological stability surface.

Abstract: Intrusion features of a landing page associated with sponsored content are identified. A feature score for the landing page based on the identified intrusion features is generated, and if the feature score for the landing page exceeds a feature threshold, the landing page is classified as a candidate landing page. A sponsor account associated with the candidate landing page can be suspended, or sponsored content associated with the candidate landing page can be suspended.

Abstract: The present invention relates to methods, systems, and computer readable media comprising instructions for rescaling human judgment data for one or more items of content. The method of the present invention comprises generating one or more test sets comprising one or more items of content and generating one or more benchmark sets comprising one or more items of content common to each of the test sets. Judgment data for the one or more items of content comprising the one or more test sets from one or more human editors is received. A variation correction factor and a drift correction factor are identified for each of the one or more human editors. The variation correction factor and drift correction factor associated with each respective human editor are thereafter applied to the one or more items of content comprising the test set for which each human editor provided judgment data.

Abstract: A machine learning system creates failure-susceptibility rankings for feeder cables in a utility's electrical distribution system. The machine learning system employs martingale boosting algorithms and Support Vector Machine (SVM) algorithms to generate a feeder failure prediction model, which is trained on static and dynamic feeder attribute data. Feeders are dynamically ranked by failure susceptibility and the rankings displayed to utility operators and engineers so that they can proactively service the distribution system to prevent local power outages. The feeder rankings may be used to redirect power flows and to prioritize repairs. A feedback loop is established to evaluate the responses of the electrical distribution system to field actions taken to optimize preventive maintenance programs.

Abstract: A system and method are disclosed for predictive determination of a process variable (P) in a technical installation. Measured values for the process variable (P) are recorded at predeterminable points in time. Temporally successive recorded measured values are combined to form a weighted measured value. A discrete measured value is assigned to each weighted measured value. Each weighted measured value that lies in a predeterminable tolerance band is assigned a same discrete measured value. A future temporal progression of the process variable (P) is estimated using a linear regression of the discrete measured values, and a point in time in which the process variable (P) reaches a predeterminable limit value is calculated.

Abstract: According to one aspect of one or more embodiments of the present invention, a system comprises: an HTM network executable at least in part on multiple node processing units (NPUs). In one embodiment the NPUs include one or more nodes, each of which can be executed by its NPU. In one embodiment, the present invention includes a technique for coordinating and scheduling HTM computation across one or more CPUs which (1) enables concurrent computation (2) does not require a central point of control (e.g. a controller entity that “orchestrates” the computation), (3) does not require global synchronization, (4) in some embodiments ensures that the same results are achieved whether the nodes are executed in parallel or serially.

Abstract: A method is provided for generating a resource function estimate of resource usage by an instance of a processing element configured to consume zero or more input data streams in a stream processing system having a set of available resources that comprises receiving at least one specified performance metric for the zero or more input data streams and a processing power of the set of available resources, wherein one specified performance metric is stream rate; generating a multi-part signature of executable-specific information for the processing element and a multi-part signature of context-specific information for the instance; accessing a database of resource functions to identify a static resource function corresponding to the executable-specific information and a context-dependent resource function corresponding to the context-specific information; combining the static resource function and the context-dependent resource function to form a composite resource function for the instance; and applying the res

Abstract: An hierarchical temporal memory network having at least one node configured to receive at least two variables of different properties. The at least two variables have different data types, different data sizes, or represent different physical or logical properties in the hierarchical temporal memory network. By using the node receiving variables of different properties, the hierarchical temporal memory network can be configured more flexibly and efficiently because a separate node is not needed to receive, process, and output variables of different properties.

Abstract: A computerized method of representing a dataset with a taxonomy includes obtaining a dataset containing a plurality of records; initializing a folder-set containing a plurality of folders; assigning labels to folders within the folder set; and classifying the plurality of records into the plurality of folders according to a predetermined entropic similarity condition.

Abstract: Provision is made for an information reproduction scheme which is intelligent and adjusts to the desires and needs of the user in a manner which is almost unnoticeable to the user and is associated with little or no additional effort on the part of the user, in that the selection, from pieces of information or a plurality of pieces of information, of the information to be reproduced is performed randomly on the basis of an adaptive probability distribution, and the adaptive probability distribution is adapted based on a captured behavior of the user upon the reproduction of the information selected.

Abstract: An enhanced rule engine, in which a rule-evaluated result that is received from an external rule engine based on a context transmitted to the external rule engine for evaluation by the rule and on a schema document serializing an automatically generated vocabulary transmitted to the external rule engine is output. The schema document defines the business rule based on a context description and a result description included in the vocabulary, and the context and the result are respectively specified according to the context description and the result description.