Abstract: This document describes techniques and apparatuses for determining an audience's state or interest using passive sensor data. The techniques receive sensor data that measures an audience during presentation of a media program. The techniques, based on this sensor data, then determine a state or interest of the audience members during the media program.

Abstract: This document describes techniques and apparatuses for determining a future portion of a currently presented media program. The techniques and apparatuses can receive current media reactions of one or many people to a currently presented media program and determine later portions to present in the media program based on the media reactions. In some embodiments, for example, a program can be presented live, reactions can be received during the live presentation, and the program altered on-the-fly and in real time based on those reactions. Further, the alterations can be general or tailored to a group or a particular person.

Abstract: The description relates to estimating object orientation. One example includes determining a first estimate of object orientation using a first technique and image data. In this example, a second estimate of the object orientation can be determined using a second technique and the image data. The first estimate can be corrected with the second estimate to generate a corrected object orientation estimate which can be output.

Abstract: This document describes techniques and apparatuses for determining an audience's state or interest using passive sensor data. The techniques receive sensor data that measures an audience during presentation of a media program. The techniques, based on this sensor data, then determine a state or interest of the audience members during the media program.

Abstract: This document describes techniques and apparatuses for determining an audience's state or interest using passive sensor data. The techniques receive sensor data that measures an audience during presentation of a media program. The techniques, based on this sensor data, then determine a state or interest of the audience members during the media program.

Abstract: Access to some aspect of a service may be limited until a user has invested in performing some amount of computation. Legitimate users typically have excess cycles on their machines, which can be used to perform computation at little or no cost to the user. By contrast, computation is expensive for for-profit internet abusers (e.g., spammers). These abusers typically use all of their computing resources to run “bots” that carry out their schemes, so computation increases the abuser's cost by forcing him or her to acquire new computing resources or to rent computer time. Thus, the providers of free services (e.g., web mail services, blogging sites, etc.), can allow newly registered users to use some limited form of the service upon registration. However, in order to make more extensive use of the service, the user can be asked to prove his legitimacy by investing in some amount of computation.

Abstract: The description relates to estimating object orientation. One example includes determining a first estimate of object orientation using a first technique and image data. In this example, a second estimate of the object orientation can be determined using a second technique and the image data. The first estimate can be corrected with the second estimate to generate a corrected object orientation estimate which can be output.

Abstract: This document describes techniques and apparatuses for determining an audience's state or interest using passive sensor data. The techniques receive sensor data that measures an audience during presentation of a media program. The techniques, based on this sensor data, then determine a state or interest of the audience members during the media program.

Abstract: This document describes techniques and apparatuses for determining an audience's state or interest using passive sensor data. The techniques receive sensor data that measures an audience during presentation of a media program. The techniques, based on this sensor data, then determine a state or interest of the audience members during the media program.

Abstract: The filtering of activities generated by nodes of a network while interacting with a device may be performed by evaluating the desirability of the activities (e.g., a spam or not-spam determination of email messages sent by the node) and assigning a trust rating to the node. However, nodes are often identified by network address, and an operator of a node sending undesirable activities may reassign the network address of the node in order to avoid heavy filtering. Instead, nodes may be identified as being controlled by a network entity (e.g., an autonomous system identified in a border gateway protocol routing table.) The network entity is assigned a network entity trust rating based on the trust ratings of the nodes controlled thereby, and an appropriate level of activity filtering based on the network entity trust rating may be selected for subsequent activities received from all nodes controlled by the network entity.

Abstract: Human Interaction Proofs (“HIPs”, sometimes referred to as “captchas”), may be generated automatically. An captcha specification language may be defined, which allows a captcha scheme to be defined in terms of how symbols are to be chosen and drawn, and how those symbols are obscured. The language may provide mechanisms to specify the various ways in which to obscure symbols. New captcha schemes may be generated from existing specifications, by using genetic algorithms that combine features from existing captcha schemes that have been successful. Moreover, the likelihood that a captcha scheme has been broken by attackers may be estimated by collecting data on the time that it takes existing captcha schemes to be broken, and using regression to estimate the time to breakage as a function of either the captcha's features or its measured quality.

Abstract: This document describes techniques and apparatuses for determining a future portion of a currently presented media program. The techniques and apparatuses can receive current media reactions of one or many people to a currently presented media program and determine later portions to present in the media program based on the media reactions. In some embodiments, for example, a program can be presented live, reactions can be received during the live presentation, and the program altered on-the-fly and in real time based on those reactions. Further, the alterations can be general or tailored to a group or a particular person.

Abstract: Human Interaction Proofs (“HIPs”, sometimes referred to as “captchas”), may be generated automatically. An captcha specification language may be defined, which allows a captcha scheme to be defined in terms of how symbols are to be chosen and drawn, and how those symbols are obscured. The language may provide mechanisms to specify the various ways in which to obscure symbols. New captcha schemes may be generated from existing specifications, by using genetic algorithms that combine features from existing captcha schemes that have been successful. Moreover, the likelihood that a captcha scheme has been broken by attackers may be estimated by collecting data on the time that it takes existing captcha schemes to be broken, and using regression to estimate the time to breakage as a function of either the captcha's features or its measured quality.

Abstract: This document describes techniques and apparatuses for determining a future portion of a currently presented media program. The techniques and apparatuses can receive current media reactions of one or many people to a currently presented media program and determine later portions to present in the media program based on the media reactions. In some embodiments, for example, a program can be presented live, reactions can be received during the live presentation, and the program altered on-the-fly and in real time based on those reactions. Further, the alterations can be general or tailored to a group or a particular person.

Abstract: This document describes techniques and apparatuses for controlling a media program based on a media reaction. In some embodiments, the techniques pause, mute, or stop a media program when a user leaves the room, when a user in the room is talking or is otherwise not paying attention to the program, or when a child walks into the room.

Abstract: Human Interaction Proofs (“HIPs”, sometimes referred to as “captchas”), may be generated automatically. An captcha specification language may be defined, which allows a captcha scheme to be defined in terms of how symbols are to be chosen and drawn, and how those symbols are obscured. The language may provide mechanisms to specify the various ways in which to obscure symbols. New captcha schemes may be generated from existing specifications, by using genetic algorithms that combine features from existing captcha schemes that have been successful. Moreover, the likelihood that a captcha scheme has been broken by attackers may be estimated by collecting data on the time that it takes existing captcha schemes to be broken, and using regression to estimate the time to breakage as a function of either the captcha's features or its measured quality.

Abstract: Human Interaction Proofs (“HIPs”, sometimes referred to as “captchas”), may be generated automatically. An captcha specification language may be defined, which allows a captcha scheme to be defined in terms of how symbols are to be chosen and drawn, and how those symbols are obscured. The language may provide mechanisms to specify the various ways in which to obscure symbols. New captcha schemes may be generated from existing specifications, by using genetic algorithms that combine features from existing captcha schemes that have been successful. Moreover, the likelihood that a captcha scheme has been broken by attackers may be estimated by collecting data on the time that it takes existing captcha schemes to be broken, and using regression to estimate the time to breakage as a function of either the captcha's features or its measured quality.

Abstract: This document describes techniques and apparatuses enabling a user interface for presenting a media reaction. The techniques receive media reactions of a person to a media program, such as the person laughing at one point of a comedy show, then smiling at another point, and then departing at a third point. The techniques may present these and other media reactions in a user interface through which a user may interact.

Abstract: Geo-data spam filters are described. In one or more implementations, origin data and language data of a message are evaluated to establish a score for the message indicating a likelihood that the message is spam. The evaluation includes comparing the origin data and the language data to ranked lists indicating message origins and languages with which a respective message recipient interacts positively and ranked lists indicating message origins and languages with which the respective recipient interacts negatively. Interactions of the respective recipient with previously sent messages may be tracked to form these lists. Based message, the message is filtered for delivery.

Abstract: This document describes techniques and apparatuses enabling advertisement presentation based on a current media reaction. The techniques and apparatuses can receive a current media reaction of a user watching a media program and, based on this current media reaction, determine which advertisement is likely to be effective. Further, the techniques and apparatuses may inform advertisers of a current media reaction thereby enabling the advertisers to bid on a right to present an advertisement based on that reaction. By so doing, costs for advertisements may more-accurately reflect the value of the time in which they are presented and advertisements may be more effective.