Abstract: In an example embodiment, a machine learning training pipeline is introduced that continuously monitors and processes training data having multiple transaction types using a sliding window, adding labels as they are available for the various different types of transactions in the training data. The processed training data, with the appropriate labels added, can then be utilized by any machine learning model that is being onboarded using the pipeline, without any specialized setup being necessary. Further, even if new data is added to the pipeline to aid in the training of a new model (such as data regarding a new payment type), this new data can be processed quickly and added to the existing data without requiring specialized processes by the entity requesting the new machine learning model. This allows the actual training of the new machine learning model to be accomplished very quickly, and deployment to be accomplished even faster.

Abstract: In an example embodiment, a solution is provided wherein a single machine learning model is trained and used to detect fraud across multiple payment types. More particularly, the concept of a payment scoring event (PSE) is introduced. A PSE is a data structure that stores multiple pieces of information about a transaction (or potential transaction). A mapping is then maintained between each payment type to be supported and the PSE structure. Each of these transaction types may have its own mapping indicating which fields in the PSR its own fields map to. The use of these mappings allows for additional payment methods to be introduced and supported at any time, necessitating only the creation of a mapping for an additional payment method.

Abstract: Aspects of the current disclosure include systems and methods for identifying an entity in a query image by comparing the query image with digital images in a database. In one or more embodiments, a query feature may be extracted from the query image and a set of candidate features may be extracted from a set of images in the database. In one or more embodiments, the distances between the query feature and the candidate features are calculated. A feature, which includes a set of shortest distances among the calculated distances and a distribution of the set of shortest distances, may be generated. In one or more embodiments, the feature is input to a trained model to determine whether the entity in the query image is the same entity associated with one of the set of shortest distances.

Abstract: Aspects of the current disclosure include systems and methods for identifying an entity in a query image by comparing the query image with digital images in a database. In one or more embodiments, a query feature may be extracted from the query image and a set of candidate features may be extracted from a set of images in the database. In one or more embodiments, the distances between the query feature and the candidate features are calculated. A feature, which includes a set of shortest distances among the calculated distances and a distribution of the set of shortest distances, may be generated. In one or more embodiments, the feature is input to a trained model to determine whether the entity in the query image is the same entity associated with one of the set of shortest distances.

Abstract: Described herein are systems and methods for predicting whether an entity associated with a profile in one network is the same entity that is associated with a profile in a second network, which networks may represent networks from different network services or may represent networks from the same network service. In embodiments, network graph features, including nodes and connections, may be used to predict a probability that the profiles in the two networks should be matched. In embodiments, additional or different factors may be included in the predicted probability, such as homophily, match probabilities of seed nodes, match probabilities of attribute-matched nodes, attribute-attribute co-occurrence probabilities, and the like.

Abstract: Described herein are systems and methods for predicting a metric value for an entity associated with a query node in a graph that represents a network. In embodiments, using a user's profile as the query node, a metric about that user may be estimated based, at least in part, as a function of how well connected the query node is to a whitelist of “good” users/nodes in the network, a blacklist of “bad” users/nodes in the network, or both. In embodiments, one or more nodes or edges may be weighted when determining a final score for the query node. In embodiments, the final score regarding the metric may be used to take one or more actions relative to the query node, including accepting it into a network, allowing or rejecting a transaction, assigning a classification to the node, using the final score to compute another estimate for a node, etc.

Abstract: Described herein are systems and methods for predicting a metric value for an entity associated with a query node in a graph that represents a network. In embodiments, using a user's profile as the query node, a metric about that user may be estimated based, at least in part, as a function of how well connected the query node is to a whitelist of “good” users/nodes in the network, a blacklist of “bad” users/nodes in the network, or both. In embodiments, one or more nodes or edges may be weighted when determining a final score for the query node. In embodiments, the final score regarding the metric may be used to take one or more actions relative to the query node, including accepting it into a network, allowing or rejecting a transaction, assigning a classification to the node, using the final score to compute another estimate for a node, etc.

Abstract: Embodiments of the present disclosure include systems and methods for identifying people in an image that contains more than one images of people. In embodiments, a query feature representation that represents features is extracted from each image of a person. In embodiments, each query feature representation is compared to image feature representations in a database and a set of candidate representations is selected among the image feature representations. Then, a set of user accounts that is associated with the set of candidate representations is selected. The strengths of connection in a network between user accounts in a set of candidate user accounts corresponding to an image and user accounts in a different set of candidate user accounts corresponding to a different image may be determined. In embodiments, user accounts that has the highest strength of connection are selected and used to identify the persons corresponding to the images.

Abstract: Aspects of the current disclosure include systems and methods for identifying an entity in a query image by comparing the query image with digital images in a database. In one or more embodiments, a query feature may be extracted from the query image and a set of candidate features may be extracted from a set of images in the database. In one or more embodiments, the distances between the query feature and the candidate features are calculated. A feature, which includes a set of shortest distances among the calculated distances and a distribution of the set of shortest distances, may be generated. In one or more embodiments, the feature is input to a trained model to determine whether the entity in the query image is the same entity associated with one of the set of shortest distances.

Abstract: Aspects of the current disclosure include systems and methods for identifying an entity in a query image by comparing the query image with digital images in a database. In one or more embodiments, a query feature may be extracted from the query image and a set of candidate features may be extracted from a set of images in the database. In one or more embodiments, the distances between the query feature and the candidate features are calculated. A feature, which includes a set of shortest distances among the calculated distances and a distribution of the set of shortest distances, may be generated. In one or more embodiments, the feature is input to a trained model to determine whether the entity in the query image is the same entity associated with one of the set of shortest distances.

Abstract: Embodiments herein use transfer learning paradigms to facilitate classification across entities without requiring the entities access to the other party's sensitive data. In one or more embodiments, one entity may train a model using its own data (which may include at least some non-shared data) and shares either the scores (or an intermediate representation of the scores). One or more other parties may use the scores as a feature in its own model. The scores may be considered to act as an embedding of the features but do not reveal the features. In other embodiments, parties may be used to train part of a model or participate in generating one or more nodes of a decision tree without revealing all its features. The trained models or decision trees may then be used for classifying unlabeled events or items.

Abstract: Embodiments of the present disclosure include systems and methods for identifying people in an image that contains more than one images of people. In embodiments, a query feature representation that represents features is extracted from each image of a person. In embodiments, each query feature representation is compared to image feature representations in a database and a set of candidate representations is selected among the image feature representations. Then, a set of user accounts that is associated with the set of candidate representations is selected. The strengths of connection in a network between user accounts in a set of candidate user accounts corresponding to an image and user accounts in a different set of candidate user accounts corresponding to a different image may be determined. In embodiments, user accounts that has the highest strength of connection are selected and used to identify the persons corresponding to the images.

Abstract: Embodiments of the present disclosure include systems and methods for identifying people in an image that contains more than one face images. In embodiments, a query feature vector that represents features is extracted from each face image. In embodiments, each query feature vector is compared to image feature vectors in a database and a set of candidate vectors is selected among the image feature vectors. Then, a set of user accounts that is associated with the set of candidate vectors is selected. The strengths of connection in a network between user accounts in a set of candidate user accounts corresponding to a face image and user accounts in a different set of candidate user accounts corresponding to a different face image may be determined. In embodiments, user accounts that has the highest strength of connection are selected and used to identify the persons corresponding to the face images.

Abstract: Described herein are systems and methods for predicting a metric value for an entity associated with a query node in a graph that represents a network. In embodiments, using a user's profile as the query node, a metric about that user may be estimated based, at least in part, as a function of how well connected the query node is to a whitelist of “good” users/nodes in the network, a blacklist of “bad” users/nodes in the network, or both. In embodiments, one or more nodes or edges may be weighted when determining a final score for the query node. In embodiments, the final score regarding the metric may be used to take one or more actions relative to the query node, including accepting it into a network, allowing or rejecting a transaction, assigning a classification to the node, using the final score to compute another estimate for a node, etc.

Abstract: Described herein are systems and methods for predicting a metric value for an entity associated with a query node in a graph that represents a network. In embodiments, using a user's profile as the query node, a metric about that user may be estimated based, at least in part, as a function of how well connected the query node is to a whitelist of “good” users/nodes in the network, a blacklist of “bad” users/nodes in the network, or both. In embodiments, one or more nodes or edges may be weighted when determining a final score for the query node. In embodiments, the final score regarding the metric may be used to take one or more actions relative to the query node, including accepting it into a network, allowing or rejecting a transaction, assigning a classification to the node, using the final score to compute another estimate for a node, etc.

Abstract: Aspects of the current disclosure include systems and methods for identifying an entity in a query image by comparing the query image with digital images in a database. In one or more embodiments, a query feature may be extracted from the query image and a set of candidate features may be extracted from a set of images in the database. In one or more embodiments, the distances between the query feature and the candidate features are calculated. A feature, which includes a set of shortest distances among the calculated distances and a distribution of the set of shortest distances, may be generated. In one or more embodiments, the feature is input to a trained model to determine whether the entity in the query image is the same entity associated with one of the set of shortest distances.

Abstract: Aspects of the current disclosure include systems and methods for identifying a person in a query image by comparing the query image with digital images in a face database. In embodiments, a query feature vector may be extracted from the query image and a set of candidate feature vectors may be extracted from a set of images in the face database. In embodiments, the distances between the query feature vector and the candidate feature vectors are calculated. A feature vector, which includes a set of shortest distances among the calculated distances and a distribution of the set of shortest distances, may be generated. In embodiments, the feature vector is input to a trained decision tree to determine whether the person in the query image is the same person associated with one of the set of shortest distances.

Abstract: The present invention relates to a method for applying a dispersant or other substances to a water surface. The invention also relates to a device for carrying out the method according to the present invention. Significant for the method according to the present invention is in that a nozzle hose system is used, said system comprising a hose (1) or a set of hoses, that the hose (1) or set of hoses is elevated at both ends, that one end of the hose (1) or set of hoses is based at an operation unit (6), and that the other end of the hose (1) or set of hoses is connected to a paravane (9; 109; 209) that is towed by or connected to the operation unit (6).

Abstract: A device and a method for visual monitoring and stabilization of an elongated metallic strip during continuous transport of the strip in a transport direction along a predetermined transport path, wherein the strip has been coated with a metallic layer by the strip having continuously passed through a bath of molten metal. The device includes an electromagnetic stabilizing device with at least one first pair of electromagnetic stabilizing means arranged on each side of the predetermined transport path, and a wiping device for wiping off superfluous molten metal from the strip by applying an air current in a line transversely of the transport direction of the strip and across essentially the whole width of the strip. A first image-reading apparatus takes images of the actual position of the strip in relation to the predetermined transport path. A second and third image-reading apparatus take images of the surface of the strip.

Abstract: An example embodiment of the present invention provides an apparatus comprising at least one processor; and at least one memory including computer program code, the at least one memory and the computer program code configured to, with the at least one processor, cause the apparatus to perform at least the following: obtain a representation of an appearance of a physical device type; obtain one or more current backgrounds used in a desktop of a specific physical device; and generate a device icon relating to the specific physical device by associating the obtained one or more backgrounds with the representation of appearance of the physical device type, wherein the generated device icon is displayed on a display as a user interface feature.