Abstract: Software that performs the following operations: (i) receiving descriptive information associated with a domain expert; (ii) receiving a question and a corresponding candidate answer for the question; (iii) determining a set of scoring features to be used to evaluate the candidate answer, wherein the set of scoring features includes: at least one scoring feature pertaining to the question, at least one scoring feature pertaining to the candidate answer, and at least one scoring feature pertaining to the descriptive information; (iv) receiving a score from the domain expert, wherein the score is based, at least in part, on the domain expert's evaluation of the candidate answer; (v) generating a feature vector based on the set of scoring features; (vi) cross-correlating the feature vector with the score; and (vii) clustering the domain expert with one or more other domain experts according to the cross-correlation, thereby creating a first cluster.

Abstract: Embodiments of the disclosure are directed towards pipe leak prediction systems configured to predict whether a pipe (e.g., a utility pipe carrying some substance such as waster) is likely to leak. The pipe leak prediction system may include one or more predictive models based on one or more machine learning techniques, and a predictive model can be trained using data for the characteristics of various pipes in order to determine the patterns associated with pipes without leaks and the patterns associated with pipes with leaks. A predictive model can be validated, used to construct a confusion matrix, and used to generate insights and inferences associated with the determinant variables used to make the predictions. The predictive model can be applied to data for various pipes in order to predict which of those pipes will leak. Any pipes that are identified as likely to leak can be assigned for further investigation for potential repair or preventative maintenance.

Abstract: A solution generation and planning system uses Artificial Intelligence (AI) techniques such as machine learning (ML) data models, predictive analytics and natural language processing (NLP) techniques for generating outputs to aid decision making in the domain of public infrastructure development. The problem statement is analyzed using the NLP techniques to generate word tokens which are employed in identifying issues that aid in selection of appropriate data sources from a plurality of discrete data sources. In addition, data models trained to produce probable solutions for the issue are also selected. The probable solutions are presented to the user who selects one of the probable solutions for implementation. Feedback from the implementation is also incorporated so that the data models are updated per the latest information obtained from the implementation of the user-selected solution.

Abstract: Technologies for providing convolutional neural networks are described. An analysis component determines an initial convolutional layer in a network architecture of a convolutional neural network and one or more subsequent convolutional layers in the network architecture. A replacement component replaces original convolutional kernels in the initial convolutional layer with initial sparse convolutional kernels, and replaces subsequent convolutional kernels in one or more subsequent convolutional layers with complementary sparse convolutional kernels. The complementary sparse kernels have a complementary pattern with respect to sparse kernels of a previous convolutional layer. Analyzing the network architecture and a trained model of a convolutional neural network can determine the original convolutional kernels and replace those kernels with sparse kernels based on similarity and/or weight in an initial layer, with sparse complementary kernels used in subsequent layers.

Abstract: Systems, devices, and methods of the present invention are related to determining a document classification. For example, a document classification application generates a set of discourse trees, each discourse tree corresponding to a sentence of a document and including a rhetorical relationship that relates two elementary discourse units. The document classification application creates one or more communicative discourse trees from the discourse trees by matching each elementary discourse unit in a discourse tree that has a verb to a verb signature. The document classification application combines the first communicative discourse tree and the second communicative discourse tree into a parse thicket and applies a classification model to the parse thicket in order to determine whether the document is public or private.