Abstract: A system and method for managing airtime on an out-of-home television network may include partitioning out-of-home television network airtime between a national network service provider and retailer local affiliate. Unbooked airtime may be reserved for a booking agent. In response to identifying the booking agent, the booking agent may be enabled to book reserved airtime on the out-of-home television network. Airtime may be booked with content in response to receiving a booking request. The content may be uploaded and distributed for display on the out-of-home television network.

Abstract: Clustering biometric templates is performed by determining fiduciary templates and cluster seed templates, both from a gallery of biometric templates. Similarity vectors are formed by comparing members of the cluster seed templates to the fiduciary templates. The gallery is then partitioned into clusters based upon the similarity vectors, and the clusters are populated from the remainder of the gallery. Partitioning may be performed by a classifier that implements a supervised machine learning algorithm that is trained with the similarity vectors, such as a multi-decision tree classification system. Matching may be accommodated by accessing a probe template, determining a cluster neighborhood for the probe template, and searching the cluster neighborhood to determine whether the gallery includes a match corresponding to the probe template. The same similarity metric is used both to partition a gallery into clusters, and in matching a probe template to the so-clustered gallery.

Abstract: A method, system, and computer program product for a new data visualization tool for determining distribution weights that represent values of a categorical variable and then mapping a distinct color to each of the weights so as to visually represent the different values of the categorical variable (or data attribute) in a scatter plot. The distinct colors of a splat are based on the distribution of categorical variable values in a corresponding bin, the distribution of which is represented by a vector. The vector contains as many locations as the number of different values for the categorical variable. The value stored in each location is typically a weight or percentage for that particular value of the categorical variable. Each location in the vector is also associated with a distinct color.

Abstract: A method and system for affixing covert identifying indicia to a digital image document, by altering pixel luminance and/or saturation in the image in selected areas of the image to a selected differential in luminance and/or saturation. The differential in luminance and/or saturation is selected to be in a range which is minimally detectable by the human eye. The identifying indicia may be detected by checking the digital document for patterns of differential luminance and/or saturation in the selected range of the differential.

Abstract: A method, system, and computer program product are provided for a new data visualization tool that visually approximates a scatter plot and provides smooth animation of graphics by using interpolated data. An interpolation process is performed over pre-processed bins of scattered data points. A user can smoothly animate a plot along several dimensions by using an external query device to vary a selected data attribute. If the external query device (or slider) control is positioned in between discrete positions of the slider, the displayed plot corresponds to interpolated data. Adjacent data structures are determined corresponding to the position of the external query device. The adjacent data structures are merged together, then aggregated the using the spatial columns of the data structure as a unique key. An interpolated bin is generated, where the weight of the bin is interpolated and the dependent attribute is also interpolated and weighted.

Abstract: A method, system, and computer program product are provided for visually approximating a scatter plot. Bins of scattered data points are formed. Each axis of a scatter plot is discretized according to a binning resolution. Bin positions along each discretized scatter plot axis are determined from the bin numbers. The bins, which represent a cloud of scattered data points, are volume rendered as splats. The opacity of each splat is a function of the number (count) of data points in a corresponding bin. In one example, the opacity of a splat is determined by the following equation:opacity=1-exp.sup.(-u*count),where count is the number of scattered data points in a corresponding bin, u is a global scale factor which can be varied by a slider, and exp is an exponential function. The color of the splat represents a data attribute associated with the data points in a corresponding bin.