Web GIS in practice X: a Microsoft Kinect natural user interface for Google Earth navigation

This paper covers the use of depth sensors such as Microsoft Kinect and ASUS Xtion to provide a natural user interface (NUI) for controlling 3-D (three-dimensional) virtual globes such as Google Earth (including its Street View mode), Bing Maps 3D, and NASA World Wind. The paper introduces the Microsoft Kinect device, briefly describing how it works (the underlying technology by PrimeSense), as well as its market uptake and application potential beyond its original intended purpose as a home entertainment and video game controller. The different software drivers available for connecting the Kinect device to a PC (Personal Computer) are also covered, and their comparative pros and cons briefly discussed. We survey a number of approaches and application examples for controlling 3-D virtual globes using the Kinect sensor, then describe Kinoogle, a Kinect interface for natural interaction with Google Earth, developed by students at Texas A&M University. Readers interested in trying out the application on their own hardware can download a Zip archive (included with the manuscript as additional files 1, 2, &3) that contains a 'Kinnogle installation package for Windows PCs'. Finally, we discuss some usability aspects of Kinoogle and similar NUIs for controlling 3-D virtual globes (including possible future improvements), and propose a number of unique, practical 'use scenarios' where such NUIs could prove useful in navigating a 3-D virtual globe, compared to conventional mouse/3-D mouse and keyboard-based interfaces

Knowledge Graph Empowered Machine Learning Pipelines for Improved Efficiency, Reusability, and Explainability

Artificial intelligence (AI) pipelines are complex, heavily parameterized, and expensive to execute in terms of time and computational resources. Consequently, it is onerous to run experiments with all possible parameter combinations to achieve an optimal solution. However, these AI experiments can be optimized by recommending relevant parameters to commence the experiments, reducing search space significantly, which can be fine tuned further. The relevant parameters can be identified by observing the metadata of pipelines executed in the past, and the relevant pipeline with relevant parameters can be recommended to the user. Currently, there are various metadata frameworks that automatically record the metadata of AI pipelines. Developing a recommendation system requires understanding pipeline metadata components and their interactions. There is a need to represent the metadata generated by these AI pipelines that capture the relationship among these pipeline entities. This article presents a knowledge-infused recommender that utilizes prior knowledge and metadata of already executed pipelines represented using the proposed metadata schema to recommend a relevant pipeline per user queries. Unlike black-box models, the use of knowledge graphs makes recommendations explainable, improving transparency and trustworthiness for the users