ECML PKDD 2018 Workshops
Springer International Publishing (Verlag)
978-3-030-13452-5 (ISBN)
The 20 papers presented in this volume were carefully reviewed and selected from a total of 32 submissions.
The workshops included are:
Nemesis 2018: First Workshop on Recent Advances in Adversarial Machine LearningUrbReas 2018: First International Workshop on Urban Reasoning from Complex Challenges in Cities
SoGood 2018: Third Workshop on Data Science for Social GoodIWAISe 2018: Second International Workshop on Artificial Intelligence in Security
Green Data Mining 2018: First International Workshop on Energy Efficient Data Mining and Knowledge DiscoveryLabel 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 AdversarialSetting 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.
| Erscheinungsdatum | 07.04.2019 |
|---|---|
| Reihe/Serie | Lecture Notes in Artificial Intelligence | Lecture Notes in Computer Science |
| Zusatzinfo | X, 257 p. 92 illus., 59 illus. in color. |
| Verlagsort | Cham |
| Sprache | englisch |
| Maße | 155 x 235 mm |
| Gewicht | 415 g |
| Themenwelt | Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik |
| Schlagworte | adversarial attacks • Applications • Artificial Intelligence • Computer Science • conference proceedings • Data Science • Green Computing • Image Processing • image reconstruction • Informatics • IOT • machine learning • Neural networks • privacy • Research • security • sensors • wireless sensor networks |
| ISBN-10 | 3-030-13452-0 / 3030134520 |
| ISBN-13 | 978-3-030-13452-5 / 9783030134525 |
| Zustand | Neuware |
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