Abstract: A method, a system, and an article are provided for detecting and managing anomalies associated with content presentations. An example computer-implemented method can include: obtaining a stream of data including a record of clicks on a plurality of client devices; aggregating the stream of data to generate a batch click stream for each of the client devices; processing the stream of data using a real-time anomaly detector to detect a first anomaly indicating that at least one of the client devices was previously associated with fraudulent clicks; processing each batch click stream using a batch anomaly detector to detect a second anomaly indicating that at least one of the batch click streams includes fraudulent clicks; and facilitating an adjustment of content presentations on the client devices, based on at least one of the detected first anomaly and the detected second anomaly.

Abstract: A method, a system, and an article are provided for detecting and managing anomalies associated with content presentations. An example computer-implemented method includes: obtaining a stream of data including a record of software installations on a plurality of client devices; processing the stream of data using a real-time anomaly detector to detect a first anomaly indicating that a client device was previously associated with fraudulent software installation activity; aggregating the stream of data to generate a batch record of software installations for each publisher from a plurality of content publishers; processing each batch record of software installations using a batch anomaly detector to detect a second anomaly indicating that at least one of the publishers is associated with fraudulent software installation activity; and facilitating an adjustment of content presentations on the client devices, based on at least one of the detected first anomaly and the detected second anomaly.

Abstract: A method, a system, and an article are provided for detecting bot users of a software application. An example method can include: providing a client application to a plurality of users; obtaining device-based data and application-based data for each user, the device-based data including a description of at least one computer component used to run the client application, the application-based data including a history of user interactions with the client application; aggregating the data to obtain a plurality of bot signals for each user; analyzing the bot signals to detect a bot among the plurality of users; and preventing the bot from accessing the client application.

Abstract: Detecting and managing fraud in an online system is described. An example computer-implemented method can include obtaining a plurality of signals. Each of the signals may be purported to have been generated by a different client device. The method also includes calculating a summary value for the obtained signals that indicates a measure of similarity between the obtained signals and an expected distribution of signals. The method also includes determining that the summary value represents a statistically significant deviation of the obtained signals from the expected distribution of signals. The method also includes labeling the obtained signals as fraudulently generated based on the statistically significant deviation.

Abstract: A method, a system, and an article are provided for detecting bot users of a software application. An example method can include: providing a client application to a plurality of users; obtaining device-based data and application-based data for each user, the device-based data including a description of at least one computer component used to run the client application, the application-based data including a history of user interactions with the client application; aggregating the data to obtain a plurality of bot signals for each user; analyzing the bot signals to detect a bot among the plurality of users; and preventing the bot from accessing the client application.

Abstract: A method, a system, and an article are provided for managing content presentations on a plurality of client devices. An example computer-implemented method includes: obtaining data including a record of the content presentations; aggregating the data to generate a history of content presentations for each client device; providing the history of content presentations for each client device to a plurality of weighting algorithms configured to calculate weights for the content presentations; combining the weights from each weighting algorithm for each client device; attributing, based on the combined weights for each client device, a user action taken on the client device to at least one of the content presentations; and facilitating an adjustment of future content presentations based on the attribution.

Abstract: A method, a system, and an article are provided for detecting bot users of a software application. An example method can include: providing a client application to a plurality of users; obtaining device-based data and application-based data for each user, the device-based data including a description of at least one computer component used to run the client application, the application-based data including a history of user interactions with the client application; aggregating the data to obtain a plurality of bot signals for each user; analyzing the bot signals to detect a bot among the plurality of users; and preventing the bot from accessing the client application.

Abstract: A method, a system, and an article are provided for detecting and managing anomalies associated with content presentations. An example computer-implemented method includes: obtaining a stream of data including a record of software installations on a plurality of client devices; processing the stream of data using a real-time anomaly detector to detect a first anomaly indicating that a client device was previously associated with fraudulent software installation activity; aggregating the stream of data to generate a batch record of software installations for each publisher from a plurality of content publishers; processing each batch record of software installations using a batch anomaly detector to detect a second anomaly indicating that at least one of the publishers is associated with fraudulent software installation activity; and facilitating an adjustment of content presentations on the client devices, based on at least one of the detected first anomaly and the detected second anomaly.

Abstract: A method, a system, and an article are provided for detecting and managing anomalies associated with content presentations. An example computer-implemented method can include: obtaining a stream of data including a record of clicks on a plurality of client devices; aggregating the stream of data to generate a batch click stream for each of the client devices; processing the stream of data using a real-time anomaly detector to detect a first anomaly indicating that at least one of the client devices was previously associated with fraudulent clicks; processing each batch click stream using a batch anomaly detector to detect a second anomaly indicating that at least one of the batch click streams includes fraudulent clicks; and facilitating an adjustment of content presentations on the client devices, based on at least one of the detected first anomaly and the detected second anomaly.

Abstract: On-line course offerings can be made available to users using computational techniques that reliably authenticate the identity of individual student users during the course of the very submissions and/or participation that will establish student user proficiency with course content. Authentication methods and systems include applications of behavioral biometrics.

Abstract: On-line course offerings can be made available to users using computational techniques that reliably authenticate the identity of individual student users during the course of the very submissions and/or participation that will establish student user proficiency with course content. Authentication methods and systems include applications of behavioral biometrics.

Abstract: High-quality multimedia content of on-line course offerings can be made available to users on both a free-of-direct-charge basis and on a fee-bearing subscription, member or for-credit basis, while providing a revenue split with originators and/or sponsors of educational content. In general, such compensation models rely on computational techniques that reliably authenticate the identity of individual student users during the course of the very submissions and/or participation that will establish student user proficiency with course content.

Abstract: For courses that deal with media content, such as sound, music, photographic images, hand sketches, video, conventional techniques for automatically evaluating and grading assignments are generally ill-suited to direct evaluation of coursework submitted in media-rich form. Likewise, for courses whose subject includes programming, signal processing or other functionally-expressed designs that operate on, or are used to produce media content, conventional techniques are also ill-suited. Instead, it has been discovered that media-rich, indeed even expressive, content can be accommodated as, or as derivatives of, submissions using feature extraction and machine learning techniques. In this way, e.g., in on-line course offerings, even large numbers of students and student submissions may be accommodated in a scalable and uniform grading or scoring scheme.