Abstract: The present disclosure provides an image processing method and device, which are applied to the technical field of image processing, wherein the method comprises: dividing an infrared decompression image with block artifacts into image blocks distributed in a rectangular array; acquiring boundary gray information of the image blocks, and then determining boundary drop information of each image block and its adjacent image block, thereby obtaining drop information of the image block; constructing a constraint optimization objective function of the image based on the boundary drop information, the drop information and preset compensation information; determining a target compensation matrix according to the constraint optimization objective function, wherein each element value in the target compensation matrix is a target value of compensation information of an image block, the position of which in the image is the same as the position of the element value in the target compensation matrix.

Abstract: The present disclosure provides an image processing method and device, which are applied to the technical field of image processing, wherein the method comprises: dividing an infrared decompression image with block artifacts into image blocks distributed in a rectangular array; acquiring boundary gray information of the image blocks, and then determining boundary drop information of each image block and its adjacent image block, thereby obtaining drop information of the image block; constructing a constraint optimization objective function of the image based on the boundary drop information, the drop information and preset compensation information; determining a target compensation matrix according to the constraint optimization objective function, wherein each element value in the target compensation matrix is a target value of compensation information of an image block, the position of which in the image is the same as the position of the element value in the target compensation matrix.

Abstract: A method for determining a likelihood of fraud associated with an input identity record is disclosed herein. The disclosed method contemplates determining characteristics of the input identity record by examining content of one or more fields of the input identity record. Historical identity records related to the input identity record may then be retrieved so as to define a set of linked identity records. The method further includes computing one or more network-based features of the set of linked identity records. A fraud score may then be generated based upon the characteristics of the input identity record and the one or more network-based features.

Abstract: A system for generating behavior segments and serving targeted ads. The system generates variables based on data from targeted users, incorporates recency, frequency, and velocity for the variables; optimizes the variables; converts the variables into behavior segments; and saves the behavior segments to a database. The system updates the behavior segments in real time. When a publisher requests an ad call, the system generates a score for advertisements based on the user profile, multiplies the score by the amount each advertiser is willing to pay for serving their ad, selects the highest value, and serves the ad.

Abstract: Described herein are example systems and operations for enhancing targeted advertising using A/B testing. These examples provide solutions to problems in targeted advertising, such as the problem of not being able to identify audience and/or situational targets mostly or only influenced by the ad or ad campaign of concern. For example, described herein are solutions that can build a pair of differential behavioral data sets similar to an A/B clinical study. Then two or more models can be generated on each data set. In an example, these models can be based on machine learning and/or statistical analysis. The differential learning between the two or more models can then be used to enhance predictions of desired response probabilities mostly or only due to the influence of the ad or ad campaign being modeled.

Abstract: Described herein are example systems and operations for enhancing targeted delivery of online content using action rate lift and/or A/B testing. These examples provide solutions to problems in targeted delivery of online content, such as the problem of not being able to identify audience and/or situational targets mostly or only influenced by the content item or campaign of concern. For example, described herein are solutions that can estimate AR lift associated with a content item, and then distribute the content item or similar content items accordingly. An AR lift model can be used and such a model can use machine learning, A/B testing, and/or statistical analysis.

Abstract: A bivariate metric is disclosed, in which one variable measures the variability of the estimate, and the other measures the accuracy of classifying the positive and negative users. A bagged logistic regression model is used, which achieves a comparable classification accuracy as a usual logistic regression, but a much more stable estimate of individual advertising channel contributions. Embodiments of the invention also provide an intuitive and simple probabilistic model to quantify the attribution of different advertising channels directly. Both the bagged logistic model and the probabilistic model are then applied to a real world data set from a multichannel advertising campaign.

Abstract: A system for generating behavior segments and serving targeted ads. The system generates variables based on data from targeted users, incorporates recency, frequency, and velocity for the variables; optimizes the variables; converts the variables into behavior segments; and saves the behavior segments to a database. The system updates the behavior segments in real time. When a publisher requests an ad call, the system generates a score for advertisements based on the user profile, multiplies the score by the amount each advertiser is willing to pay for serving their ad, selects the highest value, and serves the ad.

Abstract: A unified data management platform creates audience segments by combining proprietary and third party data, assists in determining what data to buy and how to manage all aspects of third party purchased data, controls data permissions by client, tracks data utilization, and attributes and reports data cost. The platform provides solutions that address how to leverage custom audience segments across multiple demand side platforms (DSPs) and multiple media channels, such as display, video, mobile, digital TV, and digital-out-of-home, and provides approaches that allow management of all aspects of Internet advertising from a custom domain.

Abstract: Computer implemented methods and systems of processing transactions to determine the risk of transaction convert high categorical information, such as text data, to low categorical information, such as category or cluster IDs. The text data may be merchant names or other textual content of the transactions, or data related to a consumer, or any other type of entity which engages in the transaction. Content mining techniques are used to provide the conversion from high to low categorical information. In operation, the resulting low categorical information is input, along with other data, into a statistical model. The statistical model provides an output of the level of risk in the transaction. Methods of converting the high categorical information to low categorical clusters, of using such information, and other aspects of the use of such clusters are disclosed.

Abstract: A system for generating behavior segments and serving targeted ads. The system generates variables based on data from targeted users, incorporates recency, frequency, and velocity for the variables; optimizes the variables; converts the variables into behavior segments; and saves the behavior segments to a database. The system updates the behavior segments in real time. When a publisher requests an ad call, the system generates a score for advertisements based on the user profile, multiplies the score by the amount each advertiser is willing to pay for serving their ad, selects the highest value, and serves the ad.

Abstract: A method for identifying a fraudulent account application includes receiving a new account application comprising a plurality of identity-related fields and linking the identity-related fields associated with the new account application with identity-related fields associated with a plurality of historical account applications. The links form a graphical pattern on which statistical analysis can be performed to determine the likelihood that the new account application is fraudulent. The statistical analysis can comprise comparing the graphical pattern to a known, or normal graphical pattern in order to detect differences, or anomalies occurring in the graphical pattern associated with the new account application.

Abstract: Computer implemented methods and systems of processing transactions to determine the risk of transaction convert high categorical information, such as text data, to low categorical information, such as category or cluster IDs. The text data may be merchant names or other textual content of the transactions, or data related to a consumer, or any other type of entity which engages in the transaction. Content mining techniques are used to provide the conversion from high to low categorical information. In operation, the resulting low categorical information is input, along with other data, into a statistical model. The statistical model provides an output of the level of risk in the transaction. Methods of converting the high categorical information to low categorical clusters, of using such information, and other aspects of the use of such clusters are disclosed.

Abstract: A method for identifying a fraudulent account application includes receiving a new account application comprising a plurality of identity-related fields and linking the identity-related fields associated with the new account application with identity-related fields associated with a plurality of historical account applications. The links form a graphical pattern on which statistical analysis can be performed to determine the likelihood that the new account application is fraudulent. The statistical analysis can comprise comparing the graphical pattern to a known, or normal graphical pattern in order to detect differences, or anomalies occurring in the graphical pattern associated with the new account application.

Abstract: A method for identifying a fraudulent account application includes receiving a new account application comprising a plurality of identity-related fields and linking the identity-related fields associated with the new account application with identity-related fields associated with a plurality of historical account applications. The links form a graphical pattern on which statistical analysis can performed to determine the likelihood that the new account application is fraudulent. The statistical analysis can comprise comparing the graphical pattern to a known, or normal graphical pattern in order to detect differences, or anomalies occurring in the graphical pattern associated with the new account application.

Abstract: Computer implemented methods and systems of processing transactions to determine the risk of transaction convert high categorical information, such as text data, to low categorical information, such as category or cluster IDs. The text data may be merchant names or other textual content of the transactions, or data related to a consumer, or any other type of entity which engages in the transaction. Content mining techniques are used to provide the conversion from high to low categorical information. In operation, the resulting low categorical information is input, along with other data, into a statistical model. The statistical model provides an output of the level of risk in the transaction. Methods of converting the high categorical information to low categorical clusters, of using such information, and other aspects of the use of such clusters are disclosed.