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199 results on '"McArdle, Gavin"'

1. A Novel Framework for Spatiotemporal POI Analysis

Author

Zarbakhsh, Negin, McArdle, Gavin, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Lotfian, Maryam, editor, and Starace, Luigi Libero Lucio, editor

Published
2024
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4. LAMSkyCam: A Low-cost and Miniature Ground-based Sky Camera

Author

Jain, Mayank, Sengar, Vishal Singh, Gollini, Isabella, Bertolotto, Michela, McArdle, Gavin, and Dev, Soumyabrata

Subjects
Physics - Instrumentation and Detectors
Abstract

Ground-based sky imagers (GSIs) are increasingly becoming popular amongst the remote sensing analysts. This is because such imagers offer fantastic alternatives to satellite measurements for the purpose of earth observations. In this paper, we propose an extremely low-cost and miniature ground-based sky camera for atmospheric study. Built using $3$D printed components and off-the-shelf components, our sky camera is lightweight and robust for use in diverse climatic conditions. With a $63^{\circ}$ field of view angle, the camera captures high resolution sky/cloud images for both day and night times at $5$ minute intervals. The camera is designed to be be mounted on a pole-like architecture and with its compact form, it can be installed at any location without requiring any change in the existing infrastructure. For remote areas, the camera has also a local backup facility from which data can be easily accessed manually. We have open-sourced the hardware design of our sky camera, and therefore researchers can easily manufacture and deploy these cameras for their respective use cases., Comment: Published in HardwareX, 2022

Published
2022

6. Mapping Slums with Medium Resolution Satellite Imagery: a Comparative Analysis of Multi-Spectral Data and Grey-level Co-occurrence Matrix Techniques

Author

de Mattos, Agatha C. H., McArdle, Gavin, and Bertolotto, Michela

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Computers and Society
Abstract

The UN-Habitat estimates that over one billion people live in slums around the world. However, state-of-the-art techniques to detect the location of slum areas employ high-resolution satellite imagery, which is costly to obtain and process. As a result, researchers have started to look at utilising free and open-access medium resolution satellite imagery. Yet, there is no clear consensus on which data preparation and machine learning approaches are the most appropriate to use with such imagery data. In this paper, we evaluate two techniques (multi-spectral data and grey-level co-occurrence matrix feature extraction) on an open-access dataset consisting of labelled Sentinel-2 images with a spatial resolution of 10 meters. Both techniques were paired with a canonical correlation forests classifier. The results show that the grey-level co-occurrence matrix performed better than multi-spectral data for all four cities. It had an average accuracy for the slum class of 97% and a mean intersection over union of 94%, while multi-spectral data had 75% and 64% for the respective metrics. These results indicate that open-access satellite imagery with a resolution of at least 10 meters may be suitable for keeping track of development goals such as the detection of slums in cities., Comment: Accepted at the 3rd Workshop on Artificial Intelligence for Social Good (IJCAI 2021)

Published
2021

7. An Extremely-low Cost Ground-Based Whole Sky Imager

Author

Jain, Mayank, Gollini, Isabella, Bertolotto, Michela, McArdle, Gavin, and Dev, Soumyabrata

Subjects
Physics - Atmospheric and Oceanic Physics
Abstract

Ground-based Whole Sky Imagers (WSIs) are increasingly being used for various remote sensing applications. While the fundamental requirements of a WSI are to make it climate-proof with an ability to capture high resolution images, cost also plays a significant role for wider scale adoption. This paper proposes an extremely low-cost alternative to the existing WSIs. In the designed model, high resolution images are captured with auto adjusting shutter speeds based on the surrounding light intensity. Furthermore, a manual data backup option using a portable memory drive is implemented for remote locations with no internet access., Comment: Published in IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2021

Published
2021

8. Bus Journey Time Prediction with Machine Learning: An Empirical Experience in Two Cities

Author

Dunne, Laura, Rocco Di Torrepadula, Franca, Di Martino, Sergio, McArdle, Gavin, Nardone, Davide, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Mostafavi, Mir Abolfazl, editor, and Del Mondo, Géraldine, editor

Published
2023
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9. Bus Journey Time Prediction: A Comparison of Whole Route and Segment Journey Time Predictions Using Machine Learning

Author

Dunne, Laura, McArdle, Gavin, Akan, Ozgur, Editorial Board Member, Bellavista, Paolo, Editorial Board Member, Cao, Jiannong, Editorial Board Member, Coulson, Geoffrey, Editorial Board Member, Dressler, Falko, Editorial Board Member, Ferrari, Domenico, Editorial Board Member, Gerla, Mario, Editorial Board Member, Kobayashi, Hisashi, Editorial Board Member, Palazzo, Sergio, Editorial Board Member, Sahni, Sartaj, Editorial Board Member, Shen, Xuemin, Editorial Board Member, Stan, Mircea, Editorial Board Member, Jia, Xiaohua, Editorial Board Member, Zomaya, Albert Y., Editorial Board Member, Martins, Ana Lucia, editor, Ferreira, Joao C., editor, Kocian, Alexander, editor, and Tokkozhina, Ulpan, editor

Published
2023
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11. Assessing the Quality of Gridded Population Data for Quantifying the Population Living in Deprived Communities

Author

de Mattos, Agatha C. H., McArdle, Gavin, and Bertolotto, Michela

Subjects
Computer Science - Computers and Society
Abstract

Over a billion people live in slums in settlements that are often located in ecologically sensitive areas and hence highly vulnerable. This is a problem in many parts of the world, but it is more prominent in low-income countries, where in 2014 on average 65% of the urban population lived in slums. As a result, building resilient communities requires quantifying the population living in these deprived areas and improving their living conditions. However, most of the data about slums comes from census data, which is only available at aggregate levels and often excludes these settlements. Consequently, researchers have looked at alternative approaches. These approaches, however, commonly rely on expensive high-resolution satellite imagery and field-surveys, which hinders their large-scale applicability. In this paper, we investigate a cost-effective methodology to estimate the slum population by assessing the quality of gridded population data. We evaluate the accuracy of the WorldPOP and LandScan population layers against ground-truth data composed of 1,703 georeferenced polygons that were mapped as deprived areas and which had their population surveyed during the 2010 Brazilian census. While the LandScan data did not produce satisfactory results for most polygons, the WorldPOP estimates were less than 20% off for 67% of the polygons and the overall error for the totality of the studied area was only -5.9%. This small error margin demonstrates that population layers with a resolution of at least a 100m, such as WorldPOP's, can be useful tools to estimate the population living in slums., Comment: Presented at NeurIPS 2020 Workshop on Machine Learning for the Developing World

Published
2020

25. Automatic Generation of an Adaptive WebGIS

Author

Di Martino, Sergio, Ferrucci, Filomena, McArdle, Gavin, Petillo, Giacomo, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Carswell, James D., editor, Fotheringham, A. Stewart, editor, and McArdle, Gavin, editor

Published
2009
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26. State of the Art in Patterns for Point Cluster Analysis

Author

Etienne, Laurent, Devogele, Thomas, McArdle, Gavin, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Kobsa, Alfred, editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Weikum, Gerhard, editor, Murgante, Beniamino, editor, Misra, Sanjay, editor, Rocha, Ana Maria A. C., editor, Torre, Carmelo, editor, Rocha, Jorge Gustavo, editor, Falcão, Maria Irene, editor, Taniar, David, editor, Apduhan, Bernady O., editor, and Gervasi, Osvaldo, editor

Published
2014
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31. Comparing Close Destination and Route-Based Similarity Metrics for the Analysis of Map User Trajectories

Author

Tahir, Ali, McArdle, Gavin, Bertolotto, Michela, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Liang, Steve H. L., editor, Wang, Xin, editor, and Claramunt, Christophe, editor

Published
2013
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32. Interpreting Pedestrian Behaviour by Visualising and Clustering Movement Data

Author

McArdle, Gavin, Demšar, Urška, van der Spek, Stefan, McLoone, Seán, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Liang, Steve H. L., editor, Wang, Xin, editor, and Claramunt, Christophe, editor

Published
2013
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33. Recommendations Based on Region and Spatial Profiles

Author

McArdle, Gavin, Petit, Mathieu, Ray, Cyril, Claramunt, Christophe, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Di Martino, Sergio, editor, Peron, Adriano, editor, and Tezuka, Taro, editor

Published
2012
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34. A Web-Based Visualisation Tool for Analysing Mouse Movements to Support Map Personalisation

Author

Tahir, Ali, McArdle, Gavin, Bertolotto, Michela, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Xu, Jianliang, editor, Yu, Ge, editor, Zhou, Shuigeng, editor, and Unland, Rainer, editor

Published
2011
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36. Introducing Communities to e-Learning

Author

McArdle, Gavin, Monahan, Teresa, Bertolotto, Michela, van der Aalst, Will, editor, Mylopoulos, John, editor, Sadeh, Norman M., editor, Shaw, Michael J., editor, Szyperski, Clemens, editor, Filipe, Joaquim, editor, and Cordeiro, José, editor

Published
2008
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41. Assessing the Application of Three-Dimensional Collaborative Technologies within an E-Learning Environment

Author

McArdle, Gavin and Bertolotto, Michela

Abstract

Today, the Internet plays a major role in distributing learning material within third level education. Multiple online facilities provide access to educational resources. While early systems relied on webpages, which acted as repositories for learning material, nowadays sophisticated online applications manage and deliver learning resources. Courses that use solely traditional e-learning methods have high attrition rates, which are often attributed to boredom and lack of interaction with others. Indeed static text-based interfaces, where communication with others is often asynchronous, fail to stimulate students. In this article, we present a system which uses stimulating three-dimensional environments to present learning material to students, combined with multi-user and real-time technologies to permit interaction and collaboration between them. The article focuses on presenting results from a user study which indicate that these technologies can be used effectively to resolve several of the issues with existing e-learning systems. The results show that students enjoy interacting in the environment and appreciate the benefits the paradigm brings to e-learning. (Contains 3 figures and 6 tables.)

Published
2012
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43. Virtual Reality for Collaborative E-Learning

Author

Monahan, Teresa, McArdle, Gavin, and Bertolotto, Michela

Abstract

In the past, the term e-learning referred to any method of learning that used electronic delivery methods. With the advent of the Internet however, e-learning has evolved and the term is now most commonly used to refer to online courses. A multitude of systems are now available to manage and deliver learning content online. While these have proved popular, they are often single-user learning environments which provide little in the way of interaction or stimulation for the student. As the concept of lifelong learning now becomes a reality and thus more and more people are partaking in online courses, researchers are constantly exploring innovative techniques to motivate online students and enhance the e-learning experience. This article presents our research in this area and the resulting development of CLEV-R, a Collaborative Learning Environment with Virtual Reality. This web-based system uses Virtual Reality (VR) and multimedia and provides communication tools to support collaboration among students. In this article, we describe the features of CLEV-R, its adaptation for mobile devices and present the findings from an initial evaluation.

Published
2008
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