Learning a Policy for Opportunistic Active Learning

Active learning identifies data points to label that are expected to be the most useful in improving a supervised model. Opportunistic active learning incorporates active learning into interactive tasks that constrain possible queries during interactions. Prior work has shown that opportunistic active learning can be used to improve grounding of natural language descriptions in an interactive object retrieval task. In this work, we use reinforcement learning for such an object retrieval task, to learn a policy that effectively trades off task completion with model improvement that would benefit future tasks.Comment: EMNLP 2018 Camera Read

Analysis of interaction and co-editing patterns amongst OpenStreetMap contributors

OpenStreetMap (OSM) is a very well known and popular Volunteered Geographic Information (VGI) project on the Internet. In January 2013 OSM gained its one millionth registered member. Several studies have shown that only a small percentage of these registered members carry out the large majority of the mapping and map editing work. In this article we discuss results from a social-network based analysis of seven major cities in OSM in an effort to understand if there is quantitative evidence of interaction and collaboration between OSM members in these areas. Are OSM contributors working on their own to build OSM databases in these cities or is there evidence of collaboration between OSM contributors? We find that in many cases high frequent contributors (“senior mappers”) perform very large amounts of mapping work on their own but do interact (edit/update) contributions from lower frequency contributors

OpenStreetMap standalone server as a core of system for environmental data publication for wide public in Ireland

In this paper is possible to find info about system developed by EPA Environmental Protection Agency and NUI3 Maynooth for presenting environmental data collected by EPA in graphical easy to understanding for wide audience form, with focus on showing them especially on simple mobile devices like most basic telephones with Java Mobile edition on board

Integrating Volunteered Geographic Information into Pervasive Health Computing Applications

In this paper we describe the potential for using Volunteered Geographic Information (VGI) in pervasive health computing. We use the OpenStreetMap project as a case-study of a successful VGI project and investigate how it can be expanded and used as a source of spatial information for pervasive computing technologies particularly in the area of access to information on healthcare services

“I Think i Discovered a Military Base in the Middle of the Ocean”—Null Island, the Most Real of Fictional Places

This paper explores Null Island, a fictional place located at 0° latitude and 0° longitude in the WGS84 (World Geodetic System 1984) geographic coordinate system. Null Island is erroneously associated with large amounts of geographic data in a wide variety of location-based services, place databases, social media and web-based maps. Whereas it was originally considered a joke within the geospatial community, this article will demonstrate implications of its existence, both technological and social in nature, promoting Null Island as a fundamental issue of geographic information that requires more widespread awareness. The article summarizes error sources that lead to data being associated with Null Island. We identify four evolutionary phases which help explain how this fictional place evolved and established itself as an entity reaching beyond the geospatial profession to the point of being discovered by the visual arts and the general population. After providing an accurate account of data that can be found at (0, 0), geospatial, technological and social implications of Null Island are discussed. Guidelines to avoid misplacing data to Null Island are provided. Since data will likely continue to appear at this location, our contribution is aimed at academics, computing professionals and the general population to promote awareness of this error source

Neuroevolution in Deep Neural Networks: Current Trends and Future Challenges

A variety of methods have been applied to the architectural configuration and learning or training of artificial deep neural networks (DNN). These methods play a crucial role in the success or failure of the DNN for most problems and applications. Evolutionary Algorithms (EAs) are gaining momentum as a computationally feasible method for the automated optimisation and training of DNNs. Neuroevolution is a term which describes these processes of automated configuration and training of DNNs using EAs. While many works exist in the literature, no comprehensive surveys currently exist focusing exclusively on the strengths and limitations of using neuroevolution approaches in DNNs. Prolonged absence of such surveys can lead to a disjointed and fragmented field preventing DNNs researchers potentially adopting neuroevolutionary methods in their own research, resulting in lost opportunities for improving performance and wider application within real-world deep learning problems. This paper presents a comprehensive survey, discussion and evaluation of the state-of-the-art works on using EAs for architectural configuration and training of DNNs. Based on this survey, the paper highlights the most pertinent current issues and challenges in neuroevolution and identifies multiple promising future research directions.Comment: 20 pages (double column), 2 figures, 3 tables, 157 reference

MVP OSM: a tool to identify areas of high quality contributor activity in OpenStreetMap

OpenStreetMap’s success continues to grow and contributions are not limited to the collection of spatial data using GPS (Global Positioning System) equipment. A very wide range of software tools developed by, and available to, the OSM community means that at present, anyone can also make a contribution through, for example, tracing aerial imagery, directly importing data, or by adding spatial information retrieved from smartphones. Consequently ‘the map’ has become increasingly rich, but the quality of the data is very often questioned and comes under scrutiny from the GIS and LBS (Location-Based Services) communities. By examining the world map generated from OpenStreetMap, it is relatively easy to identify areas which are more or less well supported in community mapping activities; a very high level of spatial detail in certain areas can indicate the quality of OSM data. MVP OSM is a software tool designed to highlight areas in OpensStreetMap where users (contributors) are dedicated to providing high levels of spatial detail. This usually correlates with the use of a GPS and on-the-ground mapping, or, at the very least, a deep local knowledge of the area and an inherent desire to see it represented in the highest level of detail on OpenStreetMap. The input to MVP OSM is an OSM XML file, which is converted by Python into a file for spatialite (the GIS extension for sqlite). Within spatialite the data is processed to create clusters and using these spatial clusters, the tool can then derive the activity of single or multiple users in that area. Vector layers and heatmaps are generated as output that can be overlaid onto OSM maps. A high level of detail can be considered a good indicator of the quality of OSM data within a given area. The MVP OSM tool hides the details of OSM XML processing, which many researchers find difficult, and processes the data to produce very useful visualizations of contributor activity in any given OSM area

Crowdsourcing: A Geographic Approach to Public Engagement, The Programmable City Working Paper 6

In this paper we examine three geographic crowdsourcing models, namely: volunteered geographic information (VGI), citizen science (CS) and participatory mapping (PM) (Goodchild, 2007; Audubon Society, 1900; and Peluso, 1995). We argue that these geographic knowledge producing practices can be adopted by governments to keep databases up to date (Budhathoki et al., 2008), to gain insight about natural resources (Conrad and Hilchey, 2011), to better understand the socio-economy of the people it governs (Johnston and Sieber, 2013) and as a form of data-based public engagement. The paper will be useful to governments and public agencies considering using geographic crowdsourcing in the future. We begin by defining VGI, CS, PM and crowdsourcing. Two typologies are then offered as methods to conceptualize these practices and the Kitchin (2014) data assemblage framework is proposed as a method by which state actors can critically examine their data infrastructures. A selection of exemplary VGI, CS and PM from Canada and the Republic of Ireland are discussed and the paper concludes with some high level recommendations for administrations considering a geographic approach to crowdsourcing

Representations of Environmental Data in Web-based GIS

The GIS community is using the vast potential of the Internet to disseminate geospatial information. Web-based GIS software and services are key components in distribution of geospatial data. Web-based GIS provide government departments, local authorities and environmental agencies with unprecedented opportunities to offer online access to their environmental information and related services for citizens. Web-based GIS offers access to information services 24 hours a day, 7 days a week, 365 days a year. In order for web-GIS to be successful in delivering environmental information the representation of the input datasets and output delivery formats/structures must be suitable to both the Internet delivery medium and the intended audience. In the majority of cases this will involve conversion and re-modelling of existing data resources. This paper discusses representations of environmental data for delivery and dissemination using web-based GIS in order to serve a variety of stakeholders : policy makers, scientists, media, and the general public. We summarise the major issues for delivering complex geospatial data about the environment using this medium. Prioritisation of metadata collection and geospatial data interoperability are crucial factors in delivering effective web-GIS tools. The INSPIRE Directive will greatly increase the number of available data sources and the use of webbased GIS for environmental information provision in the future will be discussed

Crowdsourcing: A Geographic Approach to Public Engagement, The Programmable City Working Paper 6

In this paper we examine three geographic crowdsourcing models, namely: volunteered geographic information (VGI), citizen science (CS) and participatory mapping (PM) (Goodchild, 2007; Audubon Society, 1900; and Peluso, 1995). We argue that these geographic knowledge producing practices can be adopted by governments to keep databases up to date (Budhathoki et al., 2008), to gain insight about natural resources (Conrad and Hilchey, 2011), to better understand the socio-economy of the people it governs (Johnston and Sieber, 2013) and as a form of data-based public engagement. The paper will be useful to governments and public agencies considering using geographic crowdsourcing in the future. We begin by defining VGI, CS, PM and crowdsourcing. Two typologies are then offered as methods to conceptualize these practices and the Kitchin (2014) data assemblage framework is proposed as a method by which state actors can critically examine their data infrastructures. A selection of exemplary VGI, CS and PM from Canada and the Republic of Ireland are discussed and the paper concludes with some high level recommendations for administrations considering a geographic approach to crowdsourcing