Abstract: A mechanism is provided to implement an action-object interaction detection mechanism for recognizing actions in cluttered video scenes. An object bounding box is computed around an object of interest identified in a corresponding label in an initial frame where the object of interest appears in the frame. The object bounding box is propagated from the initial frame to a subsequent frame. For the initial frame and the subsequent frame: the object bounding boxes of the initial frame and the subsequent frame are refined and cropped based on the associated refined object bounding boxes. The set of cropped frames are processed to determine a probability that an action that is to be verified from the corresponding label is being performed. Responsive to determining the probability is equal to or exceeds a verification threshold, a confirmation is provided that the action-object interaction video performs the action that is to be verified.

Abstract: An object dataset creation or modification mechanism is provided for object dataset creation or modification using a labeled action-object video. For a plurality of frames of the labeled action-object video, an identification is made of a subset of frames where a bounding box object (BBO) exists. BBOs in the subset of frames where a BBO exists are pruned to identify sufficiently distinct BBOs thereby forming a set of pruned BBOs. For each pruned BBO in the set of pruned BBOs: an information addition score is determined; the information addition score is assessed; responsive to the information addition score being positively assessed, the pruned BBO is added to an object dataset; and, responsive to the information addition score being negatively assessed, the pruned BBO is discarded.

Abstract: A mechanism is provided to implement an action-object interaction detection mechanism for recognizing actions in cluttered video scenes. An object hounding box is computed around an object of interest identified in a corresponding label in an initial frame where the object of interest appears in the frame. The object bounding box is propagated from the initial frame to a subsequent frame. For the initial frame and the subsequent frame: the object bounding boxes of the initial frame and the subsequent frame are refined and cropped based on the associated refined object bounding boxes. The set of cropped frames are processed to determine a probability that an action that is to be verified from the corresponding label is being performed. Responsive to determining the probability is equal to or exceeds a verification threshold, a confirmation is provided that the action-object interaction video performs the action that is to be verified.

Abstract: A mechanism is provided to implement an action-object interaction detection mechanism for recognizing actions in cluttered video scenes. An object bounding box is computed around an object of interest identified in a corresponding label in an initial frame where the object of interest appears in the frame. The object bounding box is propagated from the initial frame to a subsequent frame. For the initial frame and the subsequent frame: the object bounding boxes of the initial frame and the subsequent frame are refined and cropped based on the associated refined object bounding boxes. The set of cropped frames are processed to determine a probability that an action that is to be verified from the corresponding label is being performed. Responsive to determining the probability is equal to or exceeds a verification threshold, a confirmation is provided that the action-object interaction video performs the action that is to be verified.

Abstract: A method for determining an intervention response category for provisioning workflows. The method determines provisioning features of a provisioning step. The method performs outlier detection to identify and remove outliers from non-intervention data to produce a non-intervention normal data set. The method performs iterative grouping on the non-intervention normal data set to determine significant variables in the provisioning features. The method performs response mapping of provisions using results of the iterative grouping including a significance of errors and a presence of errors in the non-intervention normal data and partial intervention data to categorize the provisions into a response category.

Abstract: A system is configured to determine a dominant error causing a provisioning step to become stuck during provisioning of a machine in a cloud environment. The system includes memory for storing instructions, and a processor configured to execute said instructions to determine an inverse error frequency (IEF) value for pre-intervention errors in a set of intervention provisioning data; determine a dominant error for a provision during said provisioning step in said set of intervention provisioning data based on a pre-intervention error that has a maximum IEF value; determine a duration frequency (DuF) value for the provision at said provisioning step for provisions in a set of non-intervention provisioning data; and determine said dominant error for each provision during said provisioning step in said set of non-intervention provisioning data based on an error that resulted in DuF value.

Abstract: A method for determining an intervention response category for provisioning workflows. The method determines provisioning features of a provisioning step. The method performs outlier detection to identify and remove outliers from non-intervention data to produce a non-intervention normal data set. The method performs iterative grouping on the non-intervention normal data set to determine significant variables in the provisioning features. The method performs response mapping of provisions using results of the iterative grouping including a significance of errors and a presence of errors in the non-intervention normal data and partial intervention data to categorize the provisions into a response category.

Abstract: A system is configured to determine a dominant error causing a provisioning step to become stuck during provisioning of a machine in a cloud environment. The system includes memory for storing instructions, and a processor configured to execute said instructions to determine an inverse error frequency (IEF) value for pre-intervention errors in a set of intervention provisioning data; determine a dominant error for a provision during said provisioning step in said set of intervention provisioning data based on a pre-intervention error that has a maximum IEF value; determine a duration frequency (DuF) value for the provision at said provisioning step for provisions in a set of non-intervention provisioning data; and determine said dominant error for each provision during said provisioning step in said set of non-intervention provisioning data based on an error that resulted in DuF value.