International Business Machines Corporation

Wan-Yi Lin

Ching-Yung Lin

Yinglong Xia

Jui-Hsin Lai

Anni Rosa Coden

A method includes analyzing a preview image made by an image sensor when operating in a preview image mode to determine if the preview image contains some portion that corresponds to information that is considered not subject to being imaged. The method further includes, if it is determined that the preview image does comprise some image portion that corresponds to information that is considered not subject to being imaged, disabling the image sensor from capturing an image that would correspond to the preview image. Analyzing can include accessing a data storage that contains representations of information that is considered not subject to being imaged for comparison with image features extracted from the preview image, where the data storage is at least one of contained in a device that also contains the image sensor, or is located remotely from the device and can be accessed via a wireless connection.

Method, system and computer program product for selective image capture

Determining a number of kernels within a model is provided. A number of kernels that include data samples of a majority data class of an imbalanced training data set is determined based on a set of generated artificial data samples for a minority data class of the imbalanced training data set. The number of kernels within the model is generated based on the set of generated artificial data samples. A likelihood of the set of generated artificial data samples being included in the majority data class of the imbalanced training data set is calculated. Parameters of each kernel in the number of kernels are updated based on the likelihood of the set of generated artificial data samples being included in the majority data class of the imbalanced training data set. Each kernel in the number of kernels is adjusted based on the updated parameters.

Determining a number of kernels using imbalanced training data sets

A method for predicting insider threat includes mining electronic data of an organization corresponding to activity of an entity, determining features of the electronic data corresponding to the activity of the entity, classifying the features corresponding to the activity of the entity, determining sequences of classified features matching one or more patterns of insider threat, scoring the entity according to matches of the classified features to the one or more patterns of insider threat, and predicting an insider threat corresponding to the entity according to the score.

Insider threat prediction

Injecting generated data samples into a minority data class of an imbalanced training data set is provided. In response to receiving an input to balance the imbalanced training data set that includes a majority data class and the minority data class, a set of data samples is generated for the minority data class. A distance is calculated from each data sample in the set of generated data samples to a center of a kernel that includes a set of data samples of the majority data class. Each data sample in the set of generated data samples is stored within a corresponding distance score bucket based on the calculated distance of a data sample. Generated data samples are selected from a number of highest ranking distance score buckets. The generated data samples selected from the number of highest ranking distance score buckets are injected into the minority data class.

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Wan-Yi Lin