Abstract: A system for recognizing a plurality of assets in an environment, determining a brand associated with each of the plurality of assets using a deep neural networking model, and computing a brand visibility and a compliance metric for the brand is provided, The system (i) determines a location of a plurality of assets and type of each of the plurality of assets within the media content, (ii) determines a brand and at least one object from the brand associated with each of the plurality of assets, (iii) determines at least one attribute of the at least one determined object associated with the brand, (v) implements at least one compliance rule to the at least one attribute of the at least one object, (vi) automatically determines a brand visibility and a compliance metric for the brand within the environment based on attention sequence and heatmap corresponding to the media content.

Abstract: The system for recognizing one or more objects of a design creative within an environment, analyzing the one or more objects using a deep neural networking model and generating a modified design creative by (i) determining a location of a design creative within the media content, (iii) determining an object from the design creative, (iv) determining an attribute of the object, (v) implementing a compliance rule to the attribute of the object to determine a distinctness and an effectiveness of a brand product, (vi) generating an attention sequence and heatmap for the media content, (vii) automatically generating a first recommendation based on the compliance rule, the attention heatmap, and the attention sequence, and (viii) automatically generating a modified design creative for the environment based on the attention heatmap, the attention sequence and the generated first recommendation using the deep neural networking model.

Abstract: A system for extracting a product information of assets from the low resolution media contents taken in an environment 104 and updating a global product-master database 110 with the extracted product information is provided. An image capturing device 102 captures the media contents of assets. The product information extraction system 106 that (i) receives the media contents from the device 102, (ii) identifies the low resolution media contents based on size and dimensions of media contents, (iii) parses the low resolution media contents, using a system of cascading deep neural networks, to generate the product information at Stock Keeping Unit (SKU) level, (iv) transfers the product information obtained from the deep neural network system to the point of sale device 108 to update a local point-of-sale product-master, and (v) transmits the product information to the a global product-master server to update the global product-master database 110.

Abstract: The system for recognizing one or more objects of a design creative within an environment, analyzing the one or more objects using a deep neural networking model and generating a modified design creative by (i) determining a location of a design creative within the media content, (iii) determining an object from the design creative, (iv) determining an attribute of the object, (v) implementing a compliance rule to the attribute of the object to determine a distinctness and an effectiveness of a brand product, (vi) generating an attention sequence and heatmap for the media content, (vii) automatically generating a first recommendation based on the compliance rule, the attention heatmap, and the attention sequence, and (viii) automatically generating a modified design creative for the environment based on the attention heatmap, the attention sequence and the generated first recommendation using the deep neural networking model.

Abstract: A system for recognizing a plurality of assets in an environment, determining a distribution of the plurality of assets, computing a position adjacency constraint for the distribution of the plurality of assets and a rearrangement plan based on position adjacency constraints for the plurality of assets in the environment is provided. The system (i) determines a distribution of a plurality of assets and type of each of the plurality of assets within the media content, (ii) determines a brand and at least one object from the brand associated with each of the plurality of assets, (iii) determines at least one attribute of the at least one determined object associated with the brand, (iv) computes a position adjacency constraints for the distribution of the plurality of assets and (v) computes a rearrangement plan for the plurality of assets within the environment based on the computed position adjacency constraint and compliance rules.

Abstract: A system for recognizing a plurality of assets in an environment, determining a brand associated with each of the plurality of assets using a deep neural networking model, and computing a brand visibility and a compliance metric for the brand is provided, The system (i) determines a location of a plurality of assets and type of each of the plurality of assets within the media content, (ii) determines a brand and at least one object from the brand associated with each of the plurality of assets, (iii) determines at least one attribute of the at least one determined object associated with the brand, (v) implements at least one compliance rule to the at least one attribute of the at least one object, (vi) automatically determines a brand visibility and a compliance metric for the brand within the environment based on attention sequence and heatmap corresponding to the media content.

Abstract: Methods, systems, and computer program products for hierarchical DCS-aware network, service, and application function VM partitioning are provided herein. A method includes partitioning multiple functions, within a set of virtual machines distributed across a hierarchical network of two or more data centers, into a first set of functions and a second set of functions, wherein the first set is associated with a higher performance sensitivity measure than the second set, and wherein said partitioning is based on a desired performance sensitivity measure associated with the functions and data center sensitivity measures of the data centers; executing the first set of functions in a first data center associated with a higher data center sensitivity measure than the one or more additional data centers; and executing the second set of functions in a second data center associated with a lower data center sensitivity measure than the first data center.