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: Provided is a method, computer program product, and system for predicting image sharing decisions using machine learning. A processor may receive a set of annotated images and an associated text input from each user of a plurality of users. The processor may train, using the set of annotated images and the associated text input from each user, a neural network model to output an image sharing decision that is specific to a user.

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 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: Techniques for managing big data include tagging of documents and subsequent retrieval using per-subject dictionaries having entries with some entries specially designated as entities. An entity indicates that the term in the entry has special meaning, e.g., brands (trademarks/service marks), trade names, geographic identifiers or other classes of terms. A dictionary may include a non-entity entry for a term and one or more entity entries, for different entity types. The entries may also include subject-determining-power scores. The subject-determining-power scores provide an indication of the descriptive power of the term with respect to the subject of the dictionary containing the term. The same term may have entries in multiple dictionaries with different subject-determining-power scores in each of the dictionaries. The entity distinctions for a term can then be used in tagging documents and processing retrieval requests.

Abstract: Techniques for managing big data include tagging of documents and subsequent retrieval using per-subject dictionaries having entries with subject-determining-power scores. The subject-determining-power scores provide an indication of the descriptive power of the term with respect to the subject of the dictionary containing the term. The same term may have entries in multiple dictionaries with different subject-determining-power scores in each of the dictionaries. A retrieval request for one or more documents containing search terms descriptive of the one or more documents can be processed identifying a set of candidate documents tagged with subjects and optional terms, and then applying subject-determining-power scores from the multiple dictionaries for the search term to determine a subject for the search term. The method then selects the one or more documents from the candidate documents according to the subject.

Abstract: Techniques for managing big data include retrieval using per-subject dictionaries having and hierarchical information indicating affinity between the dictionaries. A retrieval request for one or more documents pertaining to one or more subjects is performed by specifying the subjects, selecting dictionaries associated with the subjects, matching the subjects to documents in the collection according to a strength of relationship between the documents and the subjects and also using the hierarchical information, to obtain a subset of the collection of documents.

Abstract: Techniques for managing big data include retrieval using per-subject dictionaries having multiple levels of sub-classification hierarchy within the subject. Entries may include subject-determining-power (SDP) scores that provide an indication of the descriptive power of the entry term with respect to the subject of the dictionary containing the term. The same term may have entries in multiple dictionaries with different SDP scores in each of the dictionaries. A retrieval request for one or more documents containing search terms descriptive of the one or more documents can be processed by identifying a set of candidate documents tagged with subjects, i.e., identifiers of per-subject dictionaries having entries corresponding to a search term, then using affinity values to adjust the aggregate score for the terms in the dictionaries. Documents are then selected for best match to the subject based on the adjusted scores.

Abstract: Techniques for managing big data include tagging of documents and subsequent retrieval using per-subject dictionaries having entries with subject-determining-power scores. The subject-determining-power scores provide an indication of the descriptive power of the term with respect to the subject of the dictionary containing the term. The same term may have entries in multiple dictionaries with different subject-determining-power scores in each of the dictionaries. A retrieval request for one or more documents containing search terms descriptive of the one or more documents can be processed identifying a set of candidate documents tagged with subjects and optional terms, and then applying subject-determining-power scores from the multiple dictionaries for the search term to determine a subject for the search term. The method then selects the one or more documents from the candidate documents according to the subject.

Abstract: Techniques for managing big data include tagging of documents and subsequent retrieval using per-subject dictionaries having entries with subject-determining-power scores. The subject-determining-power scores provide an indication of the descriptive power of the term with respect to the subject of the dictionary containing the term. The same term may have entries in multiple dictionaries with different subject-determining-power scores in each of the dictionaries. A retrieval request for one or more documents containing search terms descriptive of the one or more documents can be processed identifying a set of candidate documents tagged with subjects and optional terms, and then applying subject-determining-power scores from the multiple dictionaries for the search term to determine a subject for the search term. The method then selects the one or more documents from the candidate documents according to the subject.

Abstract: Techniques for managing big data include tagging of documents and subsequent retrieval using per-subject dictionaries having entries with some entries specially designated as entities. An entity indicates that the term in the entry has special meaning, e.g., brands (trademarks/service marks), trade names, geographic identifiers or other classes of terms. A dictionary may include a non-entity entry for a term and one or more entity entries, for different entity types. The entries may also include subject-determining-power scores. The subject-determining-power scores provide an indication of the descriptive power of the term with respect to the subject of the dictionary containing the term. The same term may have entries in multiple dictionaries with different subject-determining-power scores in each of the dictionaries. The entity distinctions for a term can then be used in tagging documents and processing retrieval requests.

Abstract: Techniques for managing big data include tagging of documents and subsequent retrieval using per-subject dictionaries having entries with some entries specially designated as entities. An entity indicates that the term in the entry has special meaning, e.g., brands (trademarks/service marks), trade names, geographic identifiers or other classes of terms. A dictionary may include a non-entity entry for a term and one or more entity entries, for different entity types. The entries may also include subject-determining-power scores. The subject-determining-power scores provide an indication of the descriptive power of the term with respect to the subject of the dictionary containing the term. The same term may have entries in multiple dictionaries with different subject-determining-power scores in each of the dictionaries. The entity distinctions for a term can then be used in tagging documents and processing retrieval requests.

Abstract: Techniques for managing big data include retrieval using per-subject dictionaries having multiple levels of sub-classification hierarchy within the subject. Entries may include subject-determining-power (SDP) scores that provide an indication of the descriptive power of the entry term with respect to the subject of the dictionary containing the term. The same term may have entries in multiple dictionaries with different SDP scores in each of the dictionaries. A retrieval request for one or more documents containing search terms descriptive of the one or more documents can be processed by identifying a set of candidate documents tagged with subjects, i.e., identifiers of per-subject dictionaries having entries corresponding to a search term, then using affinity values to adjust the aggregate score for the terms in the dictionaries. Documents are then selected for best match to the subject based on the adjusted scores.

Abstract: Techniques for managing big data include retrieval using per-subject dictionaries having multiple levels of sub-classification hierarchy within the subject. Entries may include subject-determining-power (SDP) scores that provide an indication of the descriptive power of the entry term with respect to the subject of the dictionary containing the term. The same term may have entries in multiple dictionaries with different SDP scores in each of the dictionaries. A retrieval request for one or more documents containing search terms descriptive of the one or more documents can be processed by identifying a set of candidate documents tagged with subjects, i.e., identifiers of per-subject dictionaries having entries corresponding to a search term, then using affinity values to adjust the aggregate score for the terms in the dictionaries. Documents are then selected for best match to the subject based on the adjusted scores.

Abstract: Techniques for managing big data include retrieval using per-subject dictionaries having multiple levels of sub-classification hierarchy within the subject. Entries may include subject-determining-power (SDP) scores that provide an indication of the descriptive power of the entry term with respect to the subject of the dictionary containing the term. The same term may have entries in multiple dictionaries with different SDP scores in each of the dictionaries. A retrieval request for one or more documents containing search terms descriptive of the one or more documents can be processed by identifying a set of candidate documents tagged with subjects, i.e., identifiers of per-subject dictionaries having entries corresponding to a search term, then using affinity values to adjust the aggregate score for the terms in the dictionaries. Documents are then selected for best match to the subject based on the adjusted scores.

Abstract: Techniques for managing big data include tagging of documents and subsequent retrieval using per-subject dictionaries having entries with some entries specially designated as entities. An entity indicates that the term in the entry has special meaning, e.g., brands (trademarks/service marks), trade names, geographic identifiers or other classes of terms. A dictionary may include a non-entity entry for a term and one or more entity entries, for different entity types. The entries may also include subject-determining-power scores. The subject-determining-power scores provide an indication of the descriptive power of the term with respect to the subject of the dictionary containing the term. The same term may have entries in multiple dictionaries with different subject-determining-power scores in each of the dictionaries. The entity distinctions for a term can then be used in tagging documents and processing retrieval requests.

Abstract: A method, system, and computer program product for visualizing sensitivity information in integrated circuit (IC) design are provided in the illustrative embodiments. A plurality of sensitivity information corresponding to a first component in the IC design is received, wherein the plurality of sensitivity information includes a first sensitivity information indicating a first variation in a first electrical characteristic of a group of components as a result of a variation in an electrical characteristic of the first component. A plurality of aspects of the first sensitivity information are rendered in visual form to form a first visualization. The first visualization is presented on a schematic view of the IC design in an IC design tool such that the first sensitivity information is visually associated with the first component in the IC design.