Title | ECML PKDD 2018 Workshops [electronic resource] : Nemesis 2018, UrbReas 2018, SoGood 2018, IWAISe 2018, and Green Data Mining 2018, Dublin, Ireland, September 10-14, 2018, Proceedings / edited by Carlos Alzate, Anna Monreale, Haytham Assem, Albert Bifet, Teodora Sandra Buda, Bora Caglayan, Brett Drury, Eva García-Martín, Ricard Gavaldà, Irena Koprinska, Stefan Kramer, Niklas Lavesson, Michael Madden, Ian Molloy, Maria-Irina Nicolae, Mathieu Sinn |
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Imprint | Cham : Springer International Publishing : Imprint: Springer, 2019 |
Edition | 1st ed. 2019 |
Connect to | https://doi.org/10.1007/978-3-030-13453-2 |
Descript | X, 257 p. 92 illus., 59 illus. in color. online resource |
Label Sanitization against Label Flipping Poisoning Attacks -- Limitations of the Lipschitz constant as a Defense Against Adversarial Examples -- Understanding Adversarial Space through the Lens of Attribution -- Detecting Potential Local Adversarial Examples for Human-Interpretable Defense -- Smart Cities with Deep Edges -- Computational Model for Urban Growth Using Socioeconomic Latent Parameters -- Object Geolocation from Crowdsourced Street Level Imagery -- Extending Support Vector Regression to Constraint Optimization: Application to the Reduction of Potentially Avoidable Hospitalizations -- SALER: a Data Science Solution to Detect and Prevent Corruption in Public Administration -- MaaSim: A Liveability Simulation for Improving the Quality of Life in Cities -- Designing Data-Driven Solutions to Societal Problems: Challenges and Approaches -- Host based Intrusion Detection System with Combined CNN/RNN Model -- Cyber Attacks against the PC Learning Algorithm -- Neural Networks in an Adversarial Setting and Ill-Conditioned Weight Space -- Pseudo-Random Number Generation using Generative Adversarial Networks -- Context Delegation for Context-Based Access Control -- An Information Retrieval System For CBRNe Incidents -- A Virtual Testbed for Critical Incident Investigation with Autonomous Remote Aerial Vehicle Surveying, Artificial Intelligence, and Decision Support -- Event relevancy pruning in support of energy-efficient sequential pattern mining -- How to Measure Energy Consumption in Machine Learning Algorithms