Abstract: Systems and methods for data visualization and network extraction in accordance with embodiments of the invention are illustrated. One embodiment includes a method including obtaining a graph comprising a plurality of nodes and a plurality of edges, identifying a plurality of communities in the graph, where each community includes nodes from the plurality of nodes, generating a community graph structure based on the identified communities, where the community graph includes a plurality of supernodes and a plurality of superedges, spatializing the community graph structure, unpacking the spatialized community graph structure into an unpacked graph structure comprising the plurality of nodes and the plurality of edges, where each node in the plurality of nodes is located at approximately the position of the supernode that represented it, spatializing the unpacked graph structure, and providing the spatialized unpacked graph structure.

Abstract: Disclosed are systems and methods for scenario planning by using specially programmed software engines to simulate and detect particular feature variations leading to particular outcomes based on modeling with machine learning techniques. The disclosed technology enable improved model debugging, improved simulation efficiency and accuracy, improved model explainability, improved identification of high risk or high reward scenarios, among other improvements and combinations thereof. In some embodiments, the disclosed technology implements computerized optimization techniques applied via variation generation across a dataset of test input records to optimize for feature variation along with outcome variation.

Abstract: Systems and methods for network explainability in accordance with embodiments of the invention are illustrated. In many embodiments, network structures are extracted from tabular data structures. Communities within the network structure can be identified and processed to generate rules that explain relationships in the underlying data. In various embodiments, the rules are translated into natural language for presentation to a user.

Abstract: A system and method is provided for measurements of building façade elements by combining ground-level and orthogonal imagery. The measurements of the dimension of building façade elements are based on ground-level imagery that is scaled and geo-referenced using orthogonal imagery. The method continues by creating a tabular dataset of measurements for one or more architectural elements such as siding (e.g., aluminum, vinyl, wood, brick and/or paint), windows or doors. The tabular dataset can be part of an estimate report.

Abstract: An autonomous vehicle incorporating a multimodal multi-technique signal fusion system is described herein. The signal fusion system is configured to receive at least one sensor signal that is output by at least one sensor system (multimodal), such as at least one image sensor signal from at least one camera. The at least one sensor signal is provided to a plurality of object detector modules of different types (multi-technique), such as an absolute detector module and a relative activation detector module, that generate independent directives based on the at least one sensor signal. The independent directives are fused by a signal fusion module to output a fused directive for controlling the autonomous vehicle.

Abstract: Systems and methods are disclosed for directed image capture of a subject of interest, such as a physical building. Directed image capture can produce higher quality images such as content more centrally located within an image frame (or an associated viewing device or other display), higher quality images have greater value for subsequent uses of captured images such as for information extraction or model reconstruction. Graphical guide(s) overlaid within an image frame can facilitate quality assessments for the content or the image frame itself, such as for pixel distance of the subject of interest to a centroid of the image frame (or an associated viewing device or other display), or the effect of obscuring objects. Quality assessments can further include instructions for improving the quality of the image capture for the content of interest.

Abstract: Distance measurements are received from one or more distance measurement sensors, which may be coupled to a vehicle. A three-dimensional (3D) point cloud are generated based on the distance measurements. In some cases, 3D point clouds corresponding to distance measurements from different distance measurement sensors may be combined into one 3D point cloud. A voxelized model is generated based on the 3D point cloud. An object may be detected within the voxelized model, and in some cases may be classified by object type. If the distance measurement sensors are coupled to a vehicle, the vehicle may avoid the detected object.

Abstract: Systems and methods are disclosed for scenario planning by using specially programmed software engines to simulate and detect particular feature variations leading to particular outcomes based on modeling with machine learning techniques. The disclosed technology enables improved model debugging, improved simulation efficiency and accuracy, improved model explainability, improved identification of high risk or high reward scenarios, among other improvements and combinations thereof. Computerized optimization techniques applied via variation generation across a dataset of test input records enable optimization for feature variation along with outcome variation. Moreover, the disclosed techniques may provide and/or realize a minimized variation to input data that correspond to a point of transition from one state to another state in an outcome that results from the input data, where the transition to another state is termed a “significant” variation to the output data.

Abstract: Systems and methods are disclosed for directed image capture of a subject of interest, such as a home. Directed image capture can produce higher quality images such as more centrally located within a display and/or viewfinder of an image capture device, higher quality images have greater value for subsequent uses of captured images such as for information extraction or model reconstruction. Graphical guide(s) facilitate content placement for certain positions and quality assessments for the content of interest can be calculated such as for pixel distance of the content of interest to a centroid of the display or viewfinder, or the effect of obscuring objects. Quality assessments can further include instructions for improving the quality of the image capture for the content of interest.

Abstract: Disclosed are systems and methods for scenario planning by using specially programmed software engines to simulate and detect particular feature variations leading to particular outcomes based on modeling with machine learning techniques. The disclosed technology enables improved model debugging, improved simulation efficiency and accuracy, improved model explainability, improved identification of high risk or high reward scenarios, among other improvements and combinations thereof. In some embodiments, the disclosed technology implements computerized optimization techniques applied via variation generation across a dataset of test input records to optimize for feature variation along with outcome variation.

Abstract: A system and method is provided for measurements of building facade elements by combining ground-level and orthogonal imagery. The measurements of the dimension of building facade elements are based on ground-level imagery that is scaled and geo-referenced using orthogonal imagery. The method continues by creating a tabular dataset of measurements for one or more architectural elements such as siding (e.g., aluminum, vinyl, wood, brick and/or paint), windows or doors. The tabular dataset can be part of an estimate report.

Abstract: Systems and methods for data visualization and network extraction in accordance with embodiments of the invention are illustrated. One embodiment includes a method including obtaining a graph comprising a plurality of nodes and a plurality of edges, identifying a plurality of communities in the graph, where each community includes nodes from the plurality of nodes, generating a community graph structure based on the identified communities, where the community graph includes a plurality of supernodes and a plurality of superedges, spatializing the community graph structure, unpacking the spatialized community graph structure into an unpacked graph structure comprising the plurality of nodes and the plurality of edges, where each node in the plurality of nodes is located at approximately the position of the supernode that represented it, spatializing the unpacked graph structure, and providing the spatialized unpacked graph structure.

Abstract: Systems and methods for smart instance selection in accordance with embodiments of the invention are illustrated. One embodiment includes a system for selecting explanatory instances in datasets, including a processor, and a memory, the memory containing an instance selection application that configures the processor to: obtain a dataset comprising a plurality of records, obtain a machine learning model configured to classify records, initialize an explainer model, select at least one key instance from the dataset estimated to have explanatory power when provided to the explainer model, provide the explainer model with the selected at least one key instance; and provide an explanation produced by the explainer model.

Abstract: Data visualization processes can utilize machine learning algorithms applied to visualization data structures to determine visualization parameters that most effectively provide insight into the data, and to suggest meaningful correlations for further investigation by users. In numerous embodiments, data visualization processes can automatically generate parameters that can be used to display the data in ways that will provide enhanced value. For example, dimensions can be chosen to be associated with specific visualization parameters that are easily digestible based on their importance, e.g. with higher value dimensions placed on more easily understood visualization aspects (color, coordinate, size, etc.). In a variety of embodiments, data visualization processes can automatically describe the graph using natural language by identifying regions of interest in the visualization, and generating text using natural language generation processes.

Abstract: Systems and methods for network explainability in accordance with embodiments of the invention are illustrated. In many embodiments, network structures are extracted from tabular data structures. Communities within the network structure can be identified and processed to generate rules that explain relationships in the underlying data. In various embodiments, the rules are translated into natural language for presentation to a user.

Abstract: Systems and methods for data visualization and network extraction in accordance with embodiments of the invention are illustrated. One embodiment includes a method including obtaining a graph comprising a plurality of nodes and a plurality of edges, identifying a plurality of communities in the graph, where each community includes nodes from the plurality of nodes, generating a community graph structure based on the identified communities, where the community graph includes a plurality of supernodes and a plurality of superedges, spatializing the community graph structure, unpacking the spatialized community graph structure into an unpacked graph structure comprising the plurality of nodes and the plurality of edges, where each node in the plurality of nodes is located at approximately the position of the supernode that represented it, spatializing the unpacked graph structure, and providing the spatialized unpacked graph structure.

Abstract: Data visualization processes can utilize machine learning algorithms applied to visualization data structures to determine visualization parameters that most effectively provide insight into the data, and to suggest meaningful correlations for further investigation by users. In numerous embodiments, data visualization processes can automatically generate parameters that can be used to display the data in ways that will provide enhanced value. For example, dimensions can be chosen to be associated with specific visualization parameters that are easily digestible based on their importance, e.g. with higher value dimensions placed on more easily understood visualization aspects (color, coordinate, size, etc.). In a variety of embodiments, data visualization processes can automatically describe the graph using natural language by identifying regions of interest in the visualization, and generating text using natural language generation processes.

Abstract: A system and method is provided for measurements of building facade elements by combining ground-level and orthogonal imagery. The measurements of the dimension of building facade elements are based on ground-level imagery that is scaled and geo-referenced using orthogonal imagery. The method continues by creating a tabular dataset of measurements for one or more architectural elements such as siding (e.g., aluminum, vinyl, wood, brick and/or paint), windows or doors. The tabular dataset can be part of an estimate report.

Abstract: Distance measurements are received from one or more distance measurement sensors, which may be coupled to a vehicle. A three-dimensional (3D) point cloud are generated based on the distance measurements. In some cases, 3D point clouds corresponding to distance measurements from different distance measurement sensors may be combined into one 3D point cloud. A voxelized model is generated based on the 3D point cloud. An object may be detected within the voxelized model, and in some cases may be classified by object type. If the distance measurement sensors are coupled to a vehicle, the vehicle may avoid the detected object.

Abstract: Systems and methods for dataset merging using flow structures in accordance with embodiments of the invention are illustrated. Flow structures can be generated and sent to various computing devices to generate both the front-end and back-end of a customized computing system that can perform any number of various processes including those that merge datasets. In many embodiments, machine learning and/or natural language processing can be performed by the customized application.