Abstract: A method includes applying a machine learning model to a plurality of unlabeled datapoints to produce probability distributions for labels for the plurality of unlabeled datapoints, selecting a first subset of unlabeled datapoints from the plurality of unlabeled datapoints that satisfy a criterion, and selecting a second subset of unlabeled datapoints from the first subset based on the probability distributions for labels for the unlabeled datapoints in the first subset. The second subset is smaller than the first subset. The method also includes communicating, to a user, the second subset of unlabeled datapoints, receiving, from the user, labels for the second subset of unlabeled datapoints, and training, using the received labels, the machine learning model.

Abstract: Methods, systems and computer program products for improving ground truth quality for modeling are provided. Aspects include receiving a plurality of data inputs, wherein each of the plurality of data inputs has an associated label. Aspects also include training a model based on the plurality of data inputs. Aspects also include generating a plurality of vector representations corresponding to the plurality of data inputs based on the model. Aspects also include clustering the plurality of vector representations into one or more clusters. Aspects also include identifying at least one anomalous data input based on the one or more clusters. The at least one anomalous data input can be a data input of the plurality of data inputs that is mislabeled, contributes to an ambiguous class structure or is an outlier. Aspects also include outputting a notification that provides an indication of the at least one anomalous data input.

Abstract: Methods, systems and computer program products for improving ground truth quality for modeling are provided. Aspects include receiving a plurality of data inputs, wherein each of the plurality of data inputs has an associated label. Aspects also include training a model based on the plurality of data inputs. Aspects also include generating a plurality of vector representations corresponding to the plurality of data inputs based on the model. Aspects also include clustering the plurality of vector representations into one or more clusters. Aspects also include identifying at least one anomalous data input based on the one or more clusters. The at least one anomalous data input can be a data input of the plurality of data inputs that is mislabeled, contributes to an ambiguous class structure or is an outlier. Aspects also include outputting a notification that provides an indication of the at least one anomalous data input.

Abstract: A system including a global topic model building module that builds a global topic model based on a set of data that is updated according to an activity of each user of a plurality of users, a user model building module that builds a plurality of user models, each user model being built based on an activity of a respective user, an expertise model building module that builds an expertise model for the topic based on the activity of at least one user of the plurality of users, the expertise model for the topic setting a target level of knowledge for a first user of the plurality of users, a processor to compare a user model of the first user with the expertise model for the topic; and an expertise assessment and learning recommendation module that recommends an activity associated with the set of data to the first user based on the comparison.

Abstract: A method including generating a global topic model based on a set of data that is updated according to an activity of each user of a plurality of users, the global topic model including a topic representation for a topic, generating a plurality of user models, each user model being generated based on the activity of a respective user, generating an expertise model for the topic based on the activity of at least one user of the plurality of users, the expertise model for the topic setting a target level of knowledge for a first user of the plurality of users, comparing a user model of the first user with the expertise model for the topic, the comparing being performed by a processor of a computer system, and recommending an activity associated with the set of data to the first user based on the comparison.

Abstract: A system including a global topic model building module that builds a global topic model based on a set of data that is updated according to an activity of each user of a plurality of users, a user model building module that builds a plurality of user models, each user model being built based on an activity of a respective user, an expertise model building module that builds an expertise model for the topic based on the activity of at least one user of the plurality of users, the expertise model for the topic setting a target level of knowledge for a first user of the plurality of users, a processor to compare a user model of the first user with the expertise model for the topic; and an expertise assessment and learning recommendation module that recommends an activity associated with the set of data to the first user based on the comparison.

Abstract: A method including generating a global topic model based on a set of data that is updated according to an activity of each user of a plurality of users, the global topic model including a topic representation for a topic, generating a plurality of user models, each user model being generated based on the activity of a respective user, generating an expertise model for the topic based on the activity of at least one user of the plurality of users, the expertise model for the topic setting a target level of knowledge for a first user of the plurality of users, comparing a user model of the first user with the expertise model for the topic, the comparing being performed by a processor of a computer system, and recommending an activity associated with the set of data to the first user based on the comparison.

Abstract: A system and method for server-side method for editing metadata in a file, the method including steps of: receiving from a user a request for editing the metadata in the file; presenting a window to the user for display on a user's screen wherein the window displays properties of the metadata; receiving from the user an edit to the metadata properties; and updating the metadata properties with the edit received from the user, for producing an updated metadata.