Abstract: Methods, apparatuses, or computer program products according to the present disclosure provide for service permissions scaling. In example embodiments, an apparatus receives a service request from an edge server. The apparatus may generate an authorization token based at least in part on a permissions data vector, where the authorization token is configured for access by one or more computing devices to determine whether to grant access by a first computing device associated with the requesting entity identifier to one or more resources associated with the one or more computing devices. The apparatus may then transmit the authorization token to the edge server. According to some embodiments, the authorization token may be configured for storing in an authorization token cache. In some embodiments, the authorization token may be retrieved from the authorization token cache.

Abstract: Methods, apparatuses, or computer program products according to the present disclosure provide for service permissions scaling. In example embodiments, an apparatus receives a service request from an edge server. The apparatus may generate an authorization token based at least in part on a permissions data vector, where the authorization token is configured for access by one or more computing devices to determine whether to grant access by a first computing device associated with the requesting entity identifier to one or more resources associated with the one or more computing devices. The apparatus may then transmit the authorization token to the edge server. According to some embodiments, the authorization token may be configured for storing in an authorization token cache. In some embodiments, the authorization token may be retrieved from the authorization token cache.

Abstract: Methods, apparatuses, or computer program products according to the present disclosure provide for service permissions scaling. In example embodiments, an apparatus receives a service request from an edge server. The apparatus may generate an authorization token based at least in part on a permissions data vector, where the authorization token is configured for access by one or more computing devices to determine whether to grant access by a first computing device associated with the requesting entity identifier to one or more resources associated with the one or more computing devices. The apparatus may then transmit the authorization token to the edge server. According to some embodiments, the authorization token may be configured for storing in an authorization token cache. In some embodiments, the authorization token may be retrieved from the authorization token cache.

Abstract: An automatic image annotation system receives a reference image with one or more parts annotated along with one or more query images and automatically identifies portions from the query images that are similar to the annotated parts of the reference image. The S-matrices of the reference image and the query images are obtained via singular value decomposition (SVD). Lower-dimensional images are also obtained for the reference image and the query images using a pre-trained deep learning model. The S-matrices and the lower-dimensional images of the corresponding images are combined to generate vector representations. A distance metric is calculated for the vector representation of the reference image with that of the query image. A preliminary output image with a preliminary annotation is initially generated. The preliminary annotation is further optimized to generate an optimized annotation that adequately covers the region of interest (ROI).

Abstract: A model testing system administers tests to machine learning (ML) models to test the accuracy and the robustness of the ML models. A user interface (UI) associated with the model testing system receives selections of one or more of a plurality of tests to be administered to a ML model under test. Test data produced by one or more of a plurality of testing ML models that correspond to the plurality of tests is provided to the ML model under test based on the selected tests. One or more of a generative patches test, a generative perturbations test and a counterfeit data test can be administered to the ML model under test based on the selections.

Abstract: Systems, methods, and computer-readable storage media facilitating automated testing of datasets including natural language data are disclosed. In the disclosed embodiments, rule sets may be used to condition and transform an input dataset into a format that is suitable for use with one or more artificial intelligence processes configured to extract parameters and classification information from the input dataset. The parameters and classes derived by the artificial intelligence processes may then be used to automatically generate various testing tools (e.g., scripts, test conditions, etc.) that may be executed against a test dataset, such as program code or other types of data.

Abstract: An automatic image annotation system receives a reference image with one or more parts annotated along with one or more query images and automatically identifies portions from the query images that are similar to the annotated parts of the reference image. The S-matrices of the reference image and the query images are obtained via singular value decomposition (SVD). Lower-dimensional images are also obtained for the reference image and the query images using a pre-trained deep learning model. The S-matrices and the lower-dimensional images of the corresponding images are combined to generate vector representations. A distance metric is calculated for the vector representation of the reference image with that of the query image. A preliminary output image with a preliminary annotation is initially generated. The preliminary annotation is further optimized to generate an optimized annotation that adequately covers the region of interest (ROI).

Abstract: A model testing system administers tests to machine learning (ML) models to test the accuracy and the robustness of the ML models. A user interface (UI) associated with the model testing system receives selections of one or more of a plurality of tests to be administered to a ML model under test. Test data produced by one or more of a plurality of testing ML models that correspond to the plurality of tests is provided to the ML model under test based on the selected tests. One or more of a generative patches test, a generative perturbations test and a counterfeit data test can be administered to the ML model under test based on the selections.

Abstract: Systems, methods, and computer-readable storage media facilitating automated testing of datasets including natural language data are disclosed. In the disclosed embodiments, rule sets may be used to condition and transform an input dataset into a format that is suitable for use with one or more artificial intelligence processes configured to extract parameters and classification information from the input dataset. The parameters and classes derived by the artificial intelligence processes may then be used to automatically generate various testing tools (e.g., scripts, test conditions, etc.) that may be executed against a test dataset, such as program code or other types of data.