Abstract: Methods, systems, and computer program products for a content management system. Embodiments operate within or in conjunction with such a content management system. The content management system stores content objects for access by various collaborators, including extra-system collaborators. A user of the content management system configures extra-enterprise shared link URLs that permit the extra-system collaborators to access shared content objects over an Internet connection. A shared link event graph data structure is formed based on access requests over the extra-enterprise shared link URLs. An interactive user interface module is generated by processing a shared link report query over the shared link event graph data structure to select a subject set of the extra-enterprise shared link URLs, and to generate a data set of the interactive user interface module based at least in part on results from the query. A user interacts with the interactive user interface module to generate insights.

Abstract: Facial image bucketing is disclosed, whereby a query for facial image recognition compares the facial image against existing candidate images. Rather than comparing the facial image to each candidate image, the candidate images are organized or clustered into buckets according to their facial similarities, and the facial image is then compared to the image(s) in most-likely one(s) of the buckets. The organizing uses particular selected facial features, computes distance between the facial features, and selects ones of the computed distances to determine which facial images should be organized into the same bucket.

Abstract: Facial image bucketing is disclosed, whereby a query for facial image recognition compares the facial image against existing candidate images. Rather than comparing the facial image to each candidate image, the candidate images are organized or clustered into buckets according to their facial similarities, and the facial image is then compared to the image(s) in most-likely one(s) of the buckets. The organizing uses particular selected facial features, computes distance between the facial features, and selects ones of the computed distances to determine which facial images should be organized into the same bucket.

Abstract: Facial image bucketing is disclosed, whereby a query for facial image recognition compares the facial image against existing candidate images. Rather than comparing the facial image to each candidate image, the candidate images are organized or clustered into buckets according to their facial similarities, and the facial image is then compared to the image(s) in most-likely one(s) of the buckets. The organizing uses particular selected facial features, computes distance between the facial features, and selects ones of the computed distances to determine which facial images should be organized into the same bucket.

Abstract: Generating weights for biometric tokens in probabilistic matching systems is disclosed, where these weights are generated from computations performed on matched sets and unmatched sets of a reference data set. In an embodiment, scores from a similarity scoring function are distributed among bins, and a weight is computed for each bin as the log of (the matched set ratio/the unmatched set ratio), where the ratios are computed as the number of scores in a particular bin as compared to the total size of the set. The weights may then be used subsequently with scores computed by the scoring function to assess confidence of a computed similarity score, and are directed toward making the output of the probabilistic matching system more data-driven and more accurate.

Abstract: Generating weights for biometric tokens in probabilistic matching systems is disclosed, where these weights are generated from computations performed on matched sets and unmatched sets of a reference data set. In an embodiment, scores from a similarity scoring function are distributed among bins, and a weight is computed for each bin as the log of (the matched set ratio/the unmatched set ratio), where the ratios are computed as the number of scores in a particular bin as compared to the total size of the set. The weights may then be used subsequently with scores computed by the scoring function to assess confidence of a computed similarity score, and are directed toward making the output of the probabilistic matching system more data-driven and more accurate.

Abstract: Facial image bucketing is disclosed, whereby a query for facial image recognition compares the facial image against existing candidate images. Rather than comparing the facial image to each candidate image, the candidate images are organized or clustered into buckets according to their facial similarities, and the facial image is then compared to the image(s) in most-likely one(s) of the buckets. The organizing uses particular selected facial features, computes distance between the facial features, and selects ones of the computed distances to determine which facial images should be organized into the same bucket.

Abstract: Generating weights for biometric tokens in probabilistic matching systems is disclosed, where these weights are generated from computations performed on matched sets and unmatched sets of a reference data set. In an embodiment, scores from a similarity scoring function are distributed among bins, and a weight is computed for each bin as the log of (the matched set ratio/the unmatched set ratio), where the ratios are computed as the number of scores in a particular bin as compared to the total size of the set. The weights may then be used subsequently with scores computed by the scoring function to assess confidence of a computed similarity score, and are directed toward making the output of the probabilistic matching system more data-driven and more accurate.