Abstract: A system may receive enterprise information associated with a client enterprise. The system may select, using an industry analysis model, a set of queries associated with obtaining status information that is associated with a technology profile of the client enterprise. The system may generate client data that is associated with the enterprise information and the status information. The system may convert, using a matrix factorization technique, the client data associated with the client enterprise to a client matrix. The system may convert, using the matrix factorization technique, reference data associated with reference enterprises to a reference matrix. The system may determine, based on a comparison of the client matrix and the reference matrix, a set of scores associated with technology metrics of the technology profile. The system may perform an action associated with the client enterprise based on the set of scores.

Abstract: A method is described which included receiving (S1) a number of object images (IMn) of an object (1). Each object image (IMn) corresponds to a different view direction (n). The object images include first (IM1) and second (IM2) object images corresponding to first (n1) and second (n2) directions. The method also includes determining (S2) a mesh (20) corresponding to the target region (5) of the object (1) surface (2) based on a first subset (MESH) of the number of object images (IMn) which includes two or more object images (IMn) of the number of object images (IMn). The method also includes determining (S3) diffuse (DFUV) and specular (SPUV) maps corresponding to the target region (5) of the object (1) surface (2) based on processing a second subset (REFLECT) of the object images (IMn) using a deep learning neural network model trained to estimate diffuse (DFn) and specular (SPn) albedo components based on an input image (IMn).

Abstract: Methods and server systems for computing fraud risk scores for various merchants associated with an acquirer described herein. The method performed by a server system includes accessing merchant-related transaction data including merchant-related transaction indicators associated with a merchant from a transaction database. Method includes generating a merchant-related transaction features based on the merchant-related indicators. Method includes generating via risk prediction models, for a payment transaction with the merchant, merchant health and compliance risk scores, merchant terminal risk scores, merchant chargeback risk scores, and merchant activity risk scores based on the merchant-related transaction features. Method includes facilitating transmission of a notification message to an acquirer server associated with the merchant.

Abstract: An information processing apparatus includes: a factual attribute determining unit that determines a factual attribute that can be confirmed to be a fact with respect to a user based on user-provided data having been provided by the user oneself or history data of the user; an inferred attribute determining unit which determines an inferred attribute having been inferred with respect to the user based on user-related data including at least the factual attribute related to the user; and a credit score inferring unit that infers a credit score to be set to the user based on an attribute data group including the factual attribute and the inferred attribute related to the user.

Abstract: Solutions for data unification include: receiving a data record, the data record comprising a plurality of data fields; selecting, from among the plurality of data fields, a subset of the data fields, the subset of the data fields being fewer in number than the plurality of data fields, wherein selecting the subset of the data fields comprises: applying a first rule to select at least a first one of the data fields within the data record for inclusion in the subset of the data fields; using content of the subset of the data fields, generating a stable identifier (stableID) for the data record; and inserting the stableID into a primary key data field of the data record.

Abstract: Solutions for data unification include: receiving a data record, the data record comprising a plurality of data fields; selecting, from among the plurality of data fields, a subset of the data fields, the subset of the data fields being fewer in number than the plurality of data fields, wherein selecting the subset of the data fields comprises: applying a first rule to select at least a first one of the data fields within the data record for inclusion in the subset of the data fields; using content of the subset of the data fields, generating a stable identifier (stableID) for the data record; and inserting the stableID into a primary key data field of the data record.