Abstract: A method of providing, by a computing device, access to a user of sections of an electronic document. The method includes receiving, by a computing device, a computerized image of a user accessing an electronic document. The computing device further accesses a facial recognition database and compares the computerized image to one or more entries in the facial recognition database to determine an identity of the user. The user is provided access to one or more sections of the electronic document based upon the identity of the user.

Abstract: Techniques for content generation are provided. A plurality of discriminative terms is determined based at least in part on a first plurality of documents that are related to a first concept, and a plurality of positive exemplars and a plurality of negative exemplars are identified using the plurality of discriminative terms. A first machine learning (ML) model is trained to classify images into concepts, based on the plurality of positive exemplars and the plurality of negative exemplars. A second concept related to the first concept is then determined, based on the first ML model. A second ML model is trained to generate images based on the second concept, and a first image is generated using the second ML model. The first image is then refined using a style transfer ML model that was trained using a plurality of style images.

Abstract: A method of providing, by a computing device, access to a user of sections of an electronic document. The method includes receiving, by a computing device, a computerized image of a user accessing an electronic document. The computing device further accesses a facial recognition database and compares the computerized image to one or more entries in the facial recognition database to determine an identity of the user. The user is provided access to one or more sections of the electronic document based upon the identity of the user.

Abstract: Techniques for content generation are provided. A plurality of discriminative terms is determined based at least in part on a first plurality of documents that are related to a first concept, and a plurality of positive exemplars and a plurality of negative exemplars are identified using the plurality of discriminative terms. A first machine learning (ML) model is trained to classify images into concepts, based on the plurality of positive exemplars and the plurality of negative exemplars. A second concept related to the first concept is then determined, based on the first ML model. A second ML model is trained to generate images based on the second concept, and a first image is generated using the second ML model. The first image is then refined using a style transfer ML model that was trained using a plurality of style images.

Abstract: Embodiments relate to a system, product, and method for visually presenting data based on a viewing and change history. A first exploration is created, including a first and second view of a first data version. A second exploration is created, including a third and fourth view of a second data version. A first combined view of the first and second views is created containing a first data visualization of the first data version. A second combined view of the third and fourth views is created containing a second data visualization of the second data version. The first and second combined views are compared to display the structural changes between the first and second data versions.

Abstract: Embodiments relate to visually encoding data and analyzing an associated dataset. More specifically, the embodiments relate to encoding a dynamic dataset and supporting data exploration of the dynamic dataset. In various embodiments, data and data viewing history are tracked according to defined criteria, which form a data version and viewing analysis record. The data and record can be displayed in many ways. In one embodiment, a visual display of differences between a first version and a second version of data is shown. In another embodiment, the visual display is dynamic and changes in real-time.

Abstract: Embodiments relate to visually encoding data and analyzing an associated dataset. More specifically, the embodiments relate to encoding a dynamic dataset and supporting data exploration of the dynamic dataset. In various embodiments, data and data viewing history are tracked according to defined criterion, which form a data version and viewing analysis record. The data and record can be displayed in many ways. In one embodiment, a visual display of differences between a first version and a second version of data is shown. In another embodiment, the visual display is dynamic and changes in real-time.

Abstract: Embodiments relate to a system, product, and method for visually presenting data based on a viewing and change history. A first exploration is created, including a first and second view of a first data version. A second exploration is created, including a third and fourth view of a second data version. A first combined view of the first and second views is created containing a first data visualization of the first data version. A second combined view of the third and fourth views is created containing a second data visualization of the second data version. The first and second combined views are compared to display the structural changes between the first and second data versions.

Abstract: Embodiments relate to visually encoding data and analyzing an associated dataset. More specifically, the embodiments relate to encoding a dynamic dataset and supporting data exploration of the dynamic dataset. In various embodiments, data and data viewing history are tracked according to defined criterion, which form a data version and viewing analysis record. The data and record can be displayed in many ways. In one embodiment, a visual display of differences between a first version and a second version of data is shown. In another embodiment, the visual display is dynamic and changes in real-time.

Abstract: Embodiments relate to visually encoding data and analyzing an associated dataset. More specifically, the embodiments relate to encoding a dynamic dataset and supporting data exploration of the dynamic dataset. In various embodiments, data and data viewing history are tracked according to defined criterion, which form a data version and viewing analysis record. The data and record can be displayed in many ways. In one embodiment, a visual display of differences between a first version and a second version of data is shown. In another embodiment, the visual display is dynamic and changes in real-time.

Abstract: Embodiments relate to visually encoding data and analyzing an associated dataset. More specifically, the embodiments relate to encoding a dynamic dataset and supporting data exploration of the dynamic dataset. In various embodiments, data and data viewing history are tracked according to defined criteria, which form a data version and viewing analysis record. The data and record can be displayed in many ways. In one embodiment, a visual display of differences between a first version and a second version of data is shown. In another embodiment, the visual display is dynamic and changes in real-time.

Abstract: Embodiments relate to visually encoding data and analyzing an associated dataset. More specifically, the embodiments relate to encoding a dynamic dataset and supporting data exploration of the dynamic dataset. In various embodiments, data and data viewing history are tracked according to defined criterion, which form a data version and viewing analysis record. The data and record can be displayed in many ways. In one embodiment, a visual display of differences between a first version and a second version of data is shown. In another embodiment, the visual display is dynamic and changes in real-time.

Abstract: Embodiments relate to visually encoding data and analyzing an associated dataset. More specifically, the embodiments relate to encoding a dynamic dataset and supporting data exploration of the dynamic dataset. In various embodiments, data and data viewing history are tracked according to defined criterion, which form a data version and viewing analysis record. The data and record can be displayed in many ways. In one embodiment, a visual display of differences between a first version and a second version of data is shown. In another embodiment, the visual display is dynamic and changes in real-time.

Abstract: Embodiments relate to visually encoding data and analyzing an associated dataset. More specifically, the embodiments relate to encoding a dynamic dataset and supporting data exploration of the dynamic dataset. In various embodiments, data and data viewing history are tracked according to defined criterion, which form a data version and viewing analysis record. The data and record can be displayed in many ways. In one embodiment, a visual display of differences between a first version and a second version of data is shown. In another embodiment, the visual display is dynamic and changes in real-time.