Abstract: A method identifies electric load types of a plurality of different electric loads. The method includes providing a load feature database of a plurality of different electric load types, each of the different electric load types including a first load feature vector having at least four different load features; sensing a voltage signal and a current signal for each of the different electric loads; determining a second load feature vector comprising at least four different load features from the sensed voltage signal and the sensed current signal for a corresponding one of the different electric loads; and identifying by a processor one of the different electric load types by determining a minimum distance of the second load feature vector to the first load feature vector of the different electric load types of the load feature database.

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: 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: 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: Aggregate scoring is used to help classify digital content such as content uploaded to multi-user websites (e.g., social networking websites). In one embodiment, specific categories are used that relate to a social implication of content. For example, text, images, audio or other data formats can provide communication perceived to fall into categories such as violent, abusive, rights management, pornographic or other types of communication. The categories are used to provide a raw score to items in various groupings of a site's content. Where items are related to other items such as by organizational, social, legal, data-driven, design methods, or by other principles or definitions, the related items' raw scores are aggregated to achieve a score for a particular grouping of items that reflects, at least in part, scores from two or more of the related items.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for managing decision models. In one aspect, a method includes providing data that cause presentation of a model development user interface receiving first model rule data through the user interface. The first model rule data specify a first characteristic of a violating resource and a threshold score for the first characteristic. Additional model rule data are received through the user interface. The additional model rule data specify one or more additional model rules. Relationship data are received through the user interface for each of the additional model rules. The relationship data specify sets of the additional model rules that violating resources satisfy. Data that cause a hierarchical presentation of the first model rule and the additional model rules are provided.

Abstract: An intelligent decision system and a method for making an intelligent decision are provided. The intelligent decision supporting system includes a multi-dimensional classifier comprising a plurality of classifiers that define different semantic standards and are trained based on the different semantic standards, for classifying a text by the semantic standards and for outputting a plurality of attributes of the text and a confidence rate of each of the plurality of attributes, a question submitting module for receiving the output of the multi-dimensional classifier, for forming a question based on the plurality of attributes of the text and the confidence rate of each attribute, and for submitting the question to an inference machine, the inference machine for receiving the question submitted by the question submitting module, for inquiring of a domain ontology knowledge library based on the question, and for providing an answer for the question to an decision reply module.

Abstract: Embodiments of systems and methods can determine evaluations of the quality of task results provided by judges. Certain embodiments can determine the quality evaluations with relatively little overlap of judges (e.g., two or three judges). The quality evaluations may include determining an error rate or a confidence index for a judge or for a particular judgment made by the judge. Certain embodiments may determine the quality evaluations by intercomparing judging results from multiple judges without requiring human intervention, comparison to evaluation data known (or assumed) to be accurate, or input from an external judge review panel. In some implementations, task results can be combined with a confidence score (based at least in part on the quality evaluation of the judge who provided the task result), and this information can be used as training data to improve performance of machine learning algorithms.

Abstract: Exemplary embodiments provide systems, devices, one or more non-transitory computer-readable media and computer-executable methods for managing publication of online advertising. In exemplary embodiments, computer-based publication techniques may include, but is not limited to, automatically determining whether the content of a particular web page article is suitable or unsuitable for accompaniment with one or more advertisements, automatically determining whether an advertisement is suitable or unsuitable for publication on a web page associated with a web page article, and automatically determining a category that may be used to classify the content of a web page article in order to select one or more categories of advertisements suitable for accompaniment with the web page article.

Abstract: In the area of storage management, service automation can be realized through the use of “MAPE” loop(s). A Planner (P) interacts with the Monitoring (M), Analysis (A) and Execution (E) components in a closed loop. For each new option or potential planning action the Planner (P) invokes the Analysis (A) component. The correctness, as well as effectiveness, of the planning decision is dependent on the Analysis (A) component. Embodiments can utilize an adaptive Analysis (A) component (i.e., an analysis component that can be retrained) that also associates a value of confidence and a corresponding error in the evaluation along with a predicted impact. The Planner (P) component uses this additional information for quoting the final impact of a particular planning action as part of an adaptive MAPE loop to provide improved resource utilization and resource management.

Abstract: An apparatus for classifying an audio signal configured to: estimate at least one shaping parameter value for a plurality of samples of the audio signal; generate at least one audio signal classification value by mapping the at least one shaping parameter value to one of at least two interval estimates; and determine at least one audio signal classification decision based on the at least one audio signal classification value.

Abstract: An apparatus, system, and method are disclosed for defining normal usage of a computing system resource. A method for defining normal usage of a computing system resource includes receiving a repeating schedule that represents system usage of one or more computing resources and receiving one or more demand events that will affect the system usage of the one or more computer resources. The method also automatically creates a predictive temporal profile that represents the system usage of the one or more computer resources from information comprising the repeating schedule and the one or more demand events. The predictive temporal profile is displayed for the user to review.

Abstract: A personalized page rank computation system is described herein that provides a fast MapReduce method for Monte Carlo approximation of personalized PageRank vectors of all the nodes in a graph. The method presented is both faster and less computationally intensive than existing methods, allowing a broader scope of problems to be solved by existing computing hardware. The system adopts the Monte Carlo approach and provides a method to compute single random walks of a given length for all nodes in a graph that it is superior in terms of the number of map-reduce iterations among a broad class of methods. The resulting solution reduces the I/O cost and outperforms the state-of-the-art FPPR approximation methods, in terms of efficiency and approximation error. Thus, the system can very efficiently perform single random walks of a given length starting at each node in the graph and can very efficiently approximate all the personalized PageRank vectors.

Abstract: A system is provided for automatically predicting actions a user is likely to take in response to receiving data. The system may be configured to monitor and observe a user's interactions with incoming data and to identify patterns of actions the user may take in response to the incoming data. The system may enable a trainer component and a classifier component to determine the probability a user may take a particular action and to make predictions of likely user actions based on the observations of the user and the identified pattern of the user's actions. The system may also be configured to continuously observe the user's actions to fine-tune and adjust the identified patterns of user actions and to update the probabilities of likely user actions in order increase the accuracy of the predicted user action in response to incoming data.

Abstract: In general, a method includes receiving a training data set that includes a plurality of examples, wherein each example includes one or more features and an answer, generating a plurality of modified training data sets by applying one or more filters to the training data set, each of the plurality of modified training data sets being based on a different combination of the one or more filters, training a plurality of predictive models, each of the plurality of predictive models being trained using a different modified training data set of the plurality of modified training data sets, determining a respective accuracy for each of the plurality of predictive models, identifying a most accurate predictive model based on the determined accuracies, and specifying an association between the training data set and the combination of filters used to generate the modified training data set that was used to train the most accurate predictive model.

Abstract: A method for predicting the risk of a pirate attack in a geographical area and for generating a distribution of probabilities of a pirate attack based on intelligence (INTEL) information regarding the pirates and pirate behavior combined with information regarding environmental and meteorological (METOC) conditions and information regarding shipping activity and shipping vulnerabilities. The method can also be used to predict the probability of other activities that are subject to METOC conditions, such as anti-narcotics efforts, undersea warfare operations, mine warfare operations, and human trafficking interdiction.

Abstract: An approach is provided for providing recommendation services while protecting the privacy of personal user information. The approach involves determining a request for at least one recommendation, the request specifying at least in part an anonymized user preference record. The approach also involves causing, at least in part, a comparison of the anonymized user preference record against one or more previously stored anonymized user preference records. The approach further involves causing, at least in part, a selection of the anonymized user preference record, the one or more previously stored anonymized user preference records, or a combination thereof based, at least in part, on the comparison. The approach additionally involves determining to generate the at least one recommendation based, at least in part, on the selection.

Abstract: A method and system to evaluate image-guided dental implant treatment plans with error simulation. Error sources contributing to the inaccuracy of implant cases come from impressions, stone models, radiographic guides, CT scan and its image processing, surgical guides, as well as the surgical procedures. They are translated into the deviations between the planned and actual implant positions and orientations. The error simulation is to continuously modify and update treatment plans with the possible deviations so that the treatment plans can be visually evaluated along with other evaluation tools.