Abstract: An approach for clustering large sets of categorical data involves iteratively ordering the data points, partitioning the data into blocks based on the ordering, and clustering the data points within each block, where different iterations use different orderings and, thus, different partitionings. In some embodiments, the data points are represented by multi-dimensional categorical vectors, and the orderings are based on permutations of the categorical dimensions. The iterative clustering may be repeated for multiple successive time windows to track the clusters. Various applications of the disclosed clustering approach, including for cyber security, are also described.

Abstract: An approach for clustering large sets of categorical data involves iteratively ordering the data points, partitioning the data into blocks based on the ordering, and clustering the data points within each block, where different iterations use different orderings and, thus, different partitionings. In some embodiments, the data points are represented by multi-dimensional categorical vectors, and the orderings are based on permutations of the categorical dimensions. The iterative clustering may be repeated for multiple successive time windows to track the clusters. Various applications of the disclosed clustering approach, including for cyber security, are also described.

Abstract: A user may be authenticated to access an account, computing device, or other resource based on the user's gaze pattern and neural or other physiological response(s) to one or more images or other stimuli. When the user attempts to access the resource, a computing device may obtain login gaze tracking data and measurement of a physiological condition of the user at the time that the user is viewing an image or other stimulus. Based on comparison of the login gaze tracking data and the measurement of the physiological condition to a model, the computing device can determine whether to authenticate the user to access the resource.

Abstract: A user may be authenticated to access an account, computing device, or other resource based on the user's gaze pattern and neural or other physiological response(s) to one or more images or other stimuli. When the user attempts to access the resource, a computing device may obtain login gaze tracking data and measurement of a physiological condition of the user at the time that the user is viewing an image or other stimulus. Based on comparison of the login gaze tracking data and the measurement of the physiological condition to a model, the computing device can determine whether to authenticate the user to access the resource.

Abstract: Devices are often configurable to perform actions automatically in response to a condition, such as an alarm presented at a time or date of a meeting; a message associated with a location specified by a geofence; or an automated response to a received message. Such conditions may be tangentially applied to actions involving an individual (e.g., a reminder presented during an anticipated meeting or a geofence associated with the individual's office), but may result in false positives when the individual is not actually present, and false negatives when an unanticipated presence of the individual arises. Instead, a device may be configured to detect the presence of the individual with the user (e.g., capturing a photo of the environment of the user, and identifying the face of the individual in the photo), and to perform an action for the user during the detected presence of the individual with the user.