Abstract: A wireless model rocket launching system which provides for safety checking and advanced functionality to be performed wirelessly using a mobile communication device such as a cell phone. A set of safety checks can be performed before the rocket is armed and triggered for launch. In addition, a location of the rocket can be tracked wirelessly on the mobile communication device.

Abstract: A networked wireless model rocket launching and tracking system. Rocket launches, users, and other activity can be remotely monitored and tracked, and stored in a remote database. Safety checking and advanced functionality to be performed wirelessly using remote servers to retrieve location information.

Abstract: A wireless model rocket launcher and rocket which provide for safety checking and advanced functionality to be performed wirelessly using a mobile communication device such as a cell phone. A set of safety checks can be performed before the rocket is armed and triggered for launch. In addition, a location of the rocket can be tracked wirelessly on the mobile communication device.

Abstract: The claimed subject matter includes techniques for providing an application testing service with a user interface that enables a user to evaluate performance data for computer implemented decision policies. An example method includes displaying a first reward statistic comprising an actual performance result for a policy implemented by an application. The method also includes obtaining experimental data corresponding to previously implemented policies, computing a second reward statistic for a hypothetical policy using a reward function applied to the experimental data. The method also includes displaying the second reward statistic together with the first reward statistic to enable a user to compare the first reward statistic and the second first reward statistic.

Abstract: Techniques and constructs can reduce the time required to determine solutions to optimization problems such as training of neural networks. Modifications to a computational model can be determined by a plurality of nodes operating in parallel. Quantized modification values can be transmitted between the nodes to reduce the volume of data to be transferred. The quantized values can be as small as one bit each. Quantization-error values can be stored and used in quantizing subsequent modifications. The nodes can operate in parallel and overlap computation and data transfer to further reduce the time required to determine solutions. The quantized values can be partitioned and each node can aggregate values for a corresponding partition.

Abstract: The claimed subject matter includes techniques for providing an application testing service. An example method includes receiving context information from a client system, the context information comprising parameters that describe details of a user interaction with an application under test (AUT). The method also includes receiving a set of potential actions from the client system. The method also includes identifying a selected action from the set of potential actions and sending the selected action to the client system, wherein the AUT is customized based on the selected action. The method also includes receiving reward data from the client system based on a user's interaction with the AUT. The method also includes storing the context information, the selected action, and the reward data to a log of application test data.

Abstract: The claimed subject matter includes techniques for providing an application testing service with a user interface that enables a user to evaluate performance data for computer implemented decision policies. An example method includes displaying a first reward statistic comprising an actual performance result for a policy implemented by an application. The method also includes obtaining experimental data corresponding to previously implemented policies, computing a second reward statistic for a hypothetical policy using a reward function applied to the experimental data. The method also includes displaying the second reward statistic together with the first reward statistic to enable a user to compare the first reward statistic and the second first reward statistic.

Abstract: Techniques and constructs can reduce the time required to determine solutions to optimization problems such as training of neural networks. Modifications to a computational model can be determined by a plurality of nodes operating in parallel. Quantized modification values can be transmitted between the nodes to reduce the volume of data to be transferred. The quantized values can be as small as one bit each. Quantization-error values can be stored and used in quantizing subsequent modifications. The nodes can operate in parallel and overlap computation and data transfer to further reduce the time required to determine solutions. The quantized values can be partitioned and each node can aggregate values for a corresponding partition.

Abstract: A computing device can determine a decomposition of data of actions of a first session based at least in part on a first computational model associating the actions of the first session with corresponding state values of the first session. The computing device can determine a second computational model based at least in part on the decomposition and an operation template. The computing device can receive a query via the communications interface, the query associated with the second session. The computing device can determine a state value of the second session based at least in part on the query. The computing device can operate the second computational model to determine at least one response associated with the query based at least in part on the state value of the second session. The computing device can provide an indication of the at least one response via the communications interface.

Abstract: Method, system, and programs for distributed machine learning on a cluster including a plurality of nodes are disclosed. A machine learning process is performed in each of the plurality of nodes based on a respective subset of training data to calculate a local parameter. The training data is partitioned over the plurality of nodes. A plurality of operation nodes are determined from the plurality of nodes based on a status of the machine learning process performed in each of the plurality of nodes. The plurality of operation nodes are connected to form a network topology. An aggregated parameter is generated by merging local parameters calculated in each of the plurality of operation nodes in accordance with the network topology.

Abstract: The claimed subject matter includes techniques for providing an application testing service. An example method includes receiving context information from a client system, the context information comprising parameters that describe details of a user interaction with an application under test (AUT). The method also includes receiving a set of potential actions from the client system. The method also includes identifying a selected action from the set of potential actions and sending the selected action to the client system, wherein the AUT is customized based on the selected action. The method also includes receiving reward data from the client system based on a user's interaction with the AUT. The method also includes storing the context information, the selected action, and the reward data to a log of application test data.

Abstract: Various embodiments for maintaining security and confidentiality of data and operations within a fraud detection system. Each of these embodiments utilizes a secure architecture in which: (1) access to data is limited to only approved or authorized entities; (2) confidential details in received data can be readily identified and concealed; and (3) confidential details that have become non-confidential can be identified and exposed.

Abstract: Various technologies relating to prevention of display of advertisements on pages rendered on client computing devices are described. A user participates in real-time auctions for advertisement positions on pages, where a bidding agent submits bids on behalf of the user. When the user wins an auction for an advertisement position, the advertisement position is populated with content approved by the user, thus effectively preventing another advertiser from presenting an advertisement in the advertisement position.

Abstract: Various embodiments for maintaining security and confidentiality of data and operations within a fraud detection system. Each of these embodiments utilizes a secure architecture in which: (1) access to data is limited to only approved or authorized entities; (2) confidential details in received data can be readily identified and concealed; and (3) confidential details that have become non-confidential can be identified and exposed.

Abstract: Methods and apparatus for performing computer-implemented personalized recommendations are disclosed. User information pertaining to a plurality of features of a plurality of users may be obtained. In addition, item information pertaining to a plurality of features of the plurality of items may be obtained. A plurality of sets of coefficients of a linear model may be obtained based at least in part on the user information and/or the item information such that each of the plurality of sets of coefficients corresponds to a different one of a plurality of items, where each of the plurality of sets of coefficients includes a plurality of coefficients, each of the plurality of coefficients corresponding to one of the plurality of features. In addition, at least one of the plurality of coefficients may be shared among the plurality of sets of coefficients for the plurality of items.

Abstract: Method, system, and programs for distributed machine learning on a cluster including a plurality of nodes are disclosed. A machine learning process is performed in each of the plurality of nodes based on a respective subset of training data to calculate a local parameter. The training data is partitioned over the plurality of nodes. A plurality of operation nodes are determined from the plurality of nodes based on a status of the machine learning process performed in each of the plurality of nodes. The plurality of operation nodes are connected to form a network topology. An aggregated parameter is generated by merging local parameters calculated in each of the plurality of operation nodes in accordance with the network topology.

Abstract: Techniques are provided for use in an auction in which selected content items, or advertisements, of content providers, or advertisers, are selected and served, and in which, for an item served in response to a serving opportunity, contingent upon occurrence of a specified user action, an associated provider's account is charged a first sum and an associated publisher's account is credited a second sum. Performance of particular content items may be explored, such as ones for which little or no historical performance information may be available. Content item selection may be based at least in part on an objective of acquiring learning information that can be used in prediction of future performance of the content item. The associated provider's account may be charged a sum that reflects a learning value component, but the associated publisher's account may be credited a sum that does not reflect a learning value component.

Abstract: Embodiments are directed towards employing an admission controller (AC) network device to coordinate voluntary requests by traffic source devices (TSDs) to transmit traffic over a network. The TSDs submit voluntary requests to transmit network traffic during an allocated time frame to the AC. The AC monitors historical network traffic data and, based on various allocation policies, provides permission to at least some of the TSDs in the form of a nonexclusive lease of bandwidth with a rate cap for an allocated time frame. The TSDs receiving the lease voluntarily agree to transmit traffic not exceeding the rate cap for the time frame of the lease. TSDs that receive a zero rate cap voluntarily agree not to transmit. However, urgent network traffic bypasses the AC. The allocation policies used to determine the rate cap and number of permitted senders include a reactive approach, a predictive approach, and a predictive-reactive approach.

Abstract: Embodiments are directed towards using a community of weighted results from local and global message classifiers to determine whether a message is spam. Each local classifier may receive a message that is to be evaluated to determine whether it is spam. A local classifier receives the message and performs a classification of the message. The local classifier may receive predictions of whether the message is spam from at least one global classifier. The local and global predictions are combined using, in one embodiment, a regression analysis to generate a single local message classification. Combining the local and global predictions is directed towards enabling a community of predictions to be used to classify messages. The user may then re-classify this output, which in turn is used as feedback to modify weights to the local and received global predictions for a next message.

Abstract: Methods and apparatus for performing computer-implemented personalized recommendations are disclosed. User information pertaining to a plurality of features of a plurality of users may be obtained. In addition, item information pertaining to a plurality of features of the plurality of items may be obtained. A plurality of sets of coefficients of a linear model may be obtained based at least in part on the user information and/or the item information such that each of the plurality of sets of coefficients corresponds to a different one of a plurality of items, where each of the plurality of sets of coefficients includes a plurality of coefficients, each of the plurality of coefficients corresponding to one of the plurality of features. In addition, at least one of the plurality of coefficients may be shared among the plurality of sets of coefficients for the plurality of items.