A fingerprint authentication method includes a first step of acquiring partial fingerprint measurement data for a part of a fingerprint, and a second step of calculating a matching rate by comparing the partial fingerprint measurement data with reference comparison data selected among a plurality of partial fingerprint registration data, each partial fingerprint registration data corresponding to a part of a fingerprint. The method further includes a third step of determining whether the matching rate is equal to or greater than an authentication threshold and a fourth step of determining, based on a result in the third step, a success of the authentication, or repeating the second and third steps by selecting new reference comparison data based on whether or not the matching rate is equal to or greater than a preset threshold smaller than the authentication threshold.

An analysis device includes an analysis unit configured to receive scattered light, transmitted light, fluorescence, or electromagnetic waves from an observed object located in a light irradiation region light-irradiated from a light source and analyze the observed object on the basis of a signal extracted on the basis of a time axis of an electrical signal output from a light-receiving unit configured to convert the received light or electromagnetic waves into the electrical signal.

An analysis device includes an analysis unit configured to receive scattered light, transmitted light, fluorescence, or electromagnetic waves from an observed object located in a light irradiation region light-irradiated from a light source and analyze the observed object on the basis of a signal extracted on the basis of a time axis of an electrical signal output from a light-receiving unit configured to convert the received light or electromagnetic waves into the electrical signal.

A system provides intelligent gallery management for biometrics. A first gallery is obtained that includes biometric and/or other information on a population of people. An application is identified. A subset of the population of people is identified based on the application. A second gallery is derived from the first gallery by pulling the information for the subset of the population of people without pulling the information for the population of people not in the subset. Biometric identification (such as facial recognition) for the application may then be performed using the second gallery rather than the first gallery. In this way, the system is improved as less time is required for biometric identification, fewer device resources are used, and so on.

A system and method are disclosed for training a system or a model to allow estimation of the value of livestock that is farmed for monetary gain. The various aspects of the invention include generation of data that is used to supplement or augment capture or real data, wherein the subject of the data is an animal. Labels or attributes are generated and validated.

Embodiments of the present disclosure relate to a method, an electronic device, and a computer program product for processing data. The method includes determining a reference tensor based on a tensor representing multidimensional data, where the reference tensor is associated with a target tensor. The method further includes decomposing the reference tensor to obtain multiple low-rank tensors, where a rank of each of the low-rank tensors is lower than that of the reference tensor. The method further includes determining the target tensor based on the multiple low-rank tensors so as to determine multidimensional data at a specific moment. By means of embodiments of the present disclosure, the overhead of computing resources may be reduced, and the time for processing data may be reduced.

Embodiments of the present disclosure relate to a method, an electronic device, and a computer program product for processing data. The method includes determining a reference tensor based on a tensor representing multidimensional data, where the reference tensor is associated with a target tensor. The method further includes decomposing the reference tensor to obtain multiple low-rank tensors, where a rank of each of the low-rank tensors is lower than that of the reference tensor. The method further includes determining the target tensor based on the multiple low-rank tensors so as to determine multidimensional data at a specific moment. By means of embodiments of the present disclosure, the overhead of computing resources may be reduced, and the time for processing data may be reduced.

Systems, devices, methods, and computer-readable media for evaluation and visualization of machine learning data drift. A method can include receiving a series of data indicating accuracy and confidence associated with classification of respective batches of input samples, and dynamically displaying, on the GUI, a concurrent plot of the accuracy and confidence as the series of data are received.

The present invention provides a multiclass classification apparatus and method robust to imbalanced data, which generate artificial data of a minority class on the basis of an over-sampling technique based on adversarial learning to balance imbalanced data and performs multiclass classification robust to imbalanced data by using corresponding data in class classification learning without additionally collecting data.

The present invention provides a multiclass classification apparatus and method robust to imbalanced data, which generate artificial data of a minority class on the basis of an over-sampling technique based on adversarial learning to balance imbalanced data and performs multiclass classification robust to imbalanced data by using corresponding data in class classification learning without additionally collecting data.