Abstract: The present invention is a two-part irrigation system that utilizes both groundwater and rainwater. The first system extracts water from groundwater layers by using extraction pipes filled with nanomilled sand that constantly moves water upwards through capillary action. The second is a rainwater collection and capillary irrigation system. The groundwater irrigation system consists of an external groundwater transport pipe filled with nanomilled sand. This encapsulates an empty internal transport pipe that delivers percolated water. The rainwater irrigation system consists of a collection, storage, filtration, and capillary irrigation system. Rainwater is collected by trays and a water tank, where the water is filtered through a hollow fiber membrane filter. This clean water is used as potable drinking water or for irrigation. The water volume required for irrigation is calculated based on moisture data collected by moisture detection devices.

Abstract: Non-limiting examples of the present disclosure describe a convolutional neural network (CNN) architecture configured to evaluate conversational relevance of query-response pairs. A CNN model is provided that can include a first branch, a second branch, and multilayer perceptron (MLP) layers. The first branch includes convolutional layers with dynamic pooling to process a query. The second branch includes convolutional layers with dynamic pooling to process candidate responses for the query. The query and the candidate responses are processed in parallel using the CNN model. The MLP layers are configured to rank query-response pairs based on conversational relevance.

Abstract: Non-limiting examples of the present disclosure describe a convolutional neural network (CNN) architecture configured to evaluate conversational relevance of query-response pairs. A CNN model is provided that can include a first branch, a second branch, and multilayer perceptron (MLP) layers. The first branch includes convolutional layers with dynamic pooling to process a query. The second branch includes convolutional layers with dynamic pooling to process candidate responses for the query. The query and the candidate responses are processed in parallel using the CNN model. The MLP layers are configured to rank query-response pairs based on conversational relevance.

Abstract: Techniques for generating voice with predetermined emotion type. In an aspect, semantic content and emotion type are separately specified for a speech segment to be generated. A candidate generation module generates a plurality of emotionally diverse candidate speech segments, wherein each candidate has the specified semantic content. A candidate selection module identifies an optimal candidate from amongst the plurality of candidate speech segments, wherein the optimal candidate most closely corresponds to the predetermined emotion type. In further aspects, crowd-sourcing techniques may be applied to generate the plurality of speech output candidates associated with a given semantic content, and machine-learning techniques may be applied to derive parameters for a real-time algorithm for the candidate selection module.

Abstract: An image chat application generates comments to images based on features of the images. In one example, the image chat application searches through a repository of stored image-comment pairs to identify a stored image that is similar to the image, and generates a comment to the image based on an identified stored image-comment pair. In another example, the image chat application may identify and tag particular objects that dominate an image, and may generate a comment to the image based on characteristics of those particular objects. In this second example, the image chat application further generates a comment to the image based on comments previously associated with the identified tag.

Abstract: An image chat application generates comments to images based on features of the images. In one example, the image chat application searches through a repository of stored image-comment pairs to identify a stored image that is similar to the image, and generates a comment to the image based on an identified stored image-comment pair. In another example, the image chat application may identify and tag particular objects that dominate an image, and may generate a comment to the image based on characteristics of those particular objects. In this second example, the image chat application further generates a comment to the image based on comments previously associated with the identified tag.

Abstract: Techniques for generating voice with predetermined emotion type. In an aspect, semantic content and emotion type are separately specified for a speech segment to be generated. A candidate generation module generates a plurality of emotionally diverse candidate speech segments, wherein each candidate has the specified semantic content. A candidate selection module identifies an optimal candidate from amongst the plurality of candidate speech segments, wherein the optimal candidate most closely corresponds to the predetermined emotion type. In further aspects, crowd-sourcing techniques may be applied to generate the plurality of speech output candidates associated with a given semantic content, and machine-learning techniques may be applied to derive parameters for a real-time algorithm for the candidate selection module.