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ORCIDIoT Streams/ITEM@PKDD/ECML 2020: Ghent, Belgium
- João Gama
, Sepideh Pashami, Albert Bifet, Moamar Sayed Mouchaweh, Holger Fröning, Franz Pernkopf, Gregor Schiele, Michaela Blott:
IoT Streams for Data-Driven Predictive Maintenance and IoT, Edge, and Mobile for Embedded Machine Learning - Second International Workshop, IoT Streams 2020, and First International Workshop, ITEM 2020, Co-located with ECML/PKDD 2020, Ghent, Belgium, September 14-18, 2020, Revised Selected Papers. Communications in Computer and Information Science 1325, Springer 2020, ISBN 978-3-030-66769-6
IoT Streams 2020: Stream Learning
- Bruno Veloso, João Gama:
Self Hyper-parameter Tuning for Stream Classification Algorithms. 3-13 - Minh-Huong Le Nguyen, Fabien Turgis, Pierre-Emmanuel Fayemi, Albert Bifet:
Challenges of Stream Learning for Predictive Maintenance in the Railway Sector. 14-29 - Hadi Fanaee-T, Mohamed-Rafik Bouguelia, Mahmoud Rahat, Jonathan Blixt, Harpal Singh:
CycleFootprint: A Fully Automated Method for Extracting Operation Cycles from Historical Raw Data of Multiple Sensors. 30-44 - Muhammad Atif Qureshi, Luis Miralles-Pechuán, Jason Payne, Ronan O'Malley, Brian Mac Namee:
Valve Health Identification Using Sensors and Machine Learning Methods. 45-60 - Mariana Barros, Bruno Veloso, Pedro Mota Pereira, Rita P. Ribeiro, João Gama:
Failure Detection of an Air Production Unit in Operational Context. 61-74
IoT Streams 2020: Feature Learning
- Patrick Klein, Niklas Weingarz, Ralph Bergmann:
Enhancing Siamese Neural Networks Through Expert Knowledge for Predictive Maintenance. 77-92 - Cheolhwan Oh, Junhyung Moon, Jongpil Jeong:
Explainable Process Monitoring Based on Class Activation Map: Garbage In, Garbage Out. 93-105 - Tanja Tornede, Alexander Tornede, Marcel Wever, Felix Mohr, Eyke Hüllermeier:
AutoML for Predictive Maintenance: One Tool to RUL Them All. 106-118 - Kunru Chen, Sepideh Pashami, Slawomir Nowaczyk, Emilia Johansson, Gustav Sternelöv, Thorsteinn S. Rögnvaldsson:
Forklift Truck Activity Recognition from CAN Data. 119-126 - Vandan Revanur, Ayodeji Ayibiowu, Mahmoud Rahat, Reza Khoshkangini:
Embeddings Based Parallel Stacked Autoencoder Approach for Dimensionality Reduction and Predictive Maintenance of Vehicles. 127-141
IoT Streams 2020: Unsupervised Machine Learning
- Yonas Y. Tefera, Maarten Meire, Stijn Luca, Peter Karsmakers:
Unsupervised Machine Learning Methods to Estimate a Health Indicator for Condition Monitoring Using Acoustic and Vibration Signals: A Comparison Based on a Toy Data Set from a Coffee Vending Machine. 145-159 - Pieter Bonte, Sander Vanden Hautte, Annelies Lejon, Veerle Ledoux, Filip De Turck, Sofie Van Hoecke, Femke Ongenae:
Unsupervised Anomaly Detection for Communication Networks: An Autoencoder Approach. 160-172 - Lingyun Cheng, Sadhana Sundaresh, Mohamed-Rafik Bouguelia, Onur Dikmen:
Interactive Anomaly Detection Based on Clustering and Online Mirror Descent. 173-186
ITEM 2020: Hardware
- Philipp Spilger, Eric Müller, Arne Emmel, Aron Leibfried, Christian Mauch, Christian Pehle, Johannes Weis, Oliver Breitwieser, Sebastian Billaudelle, Sebastian Schmitt, Timo C. Wunderlich, Yannik Stradmann, Johannes Schemmel:
hxtorch: PyTorch for BrainScaleS-2 - Perceptrons on Analog Neuromorphic Hardware. 189-200 - Johannes Weis, Philipp Spilger, Sebastian Billaudelle, Yannik Stradmann, Arne Emmel, Eric Müller, Oliver Breitwieser, Andreas Grübl, Joscha Ilmberger, Vitali Karasenko, Mitja Kleider, Christian Mauch, Korbinian Schreiber, Johannes Schemmel:
Inference with Artificial Neural Networks on Analog Neuromorphic Hardware. 201-212 - Dennis Rieber, Holger Fröning:
Search Space Complexity of Iteration Domain Based Instruction Embedding for Deep Learning Accelerators. 213-228 - Kevin Stehle, Günther Schindler, Holger Fröning:
On the Difficulty of Designing Processor Arrays for Deep Neural Networks. 229-240
ITEM 2020: Methods
- Laura Morán-Fernández, Eva Blanco-Mallo, Konstantinos Sechidis, Amparo Alonso-Betanzos, Verónica Bolón-Canedo:
When Size Matters: Markov Blanket with Limited Bit Depth Conditional Mutual Information. 243-255 - Christopher Cichiwskyj, Chao Qian, Gregor Schiele:
Time to Learn: Temporal Accelerators as an Embedded Deep Neural Network Platform. 256-267 - Frederik Funk, Thorsten Bucksch, Daniel Mueller-Gritschneder:
ML Training on a Tiny Microcontroller for a Self-adaptive Neural Network-Based DC Motor Speed Controller. 268-279
ITEM 2020: Quantization
- Laura Isabel Galindez Olascoaga, Wannes Meert, Nimish Shah, Marian Verhelst:
Dynamic Complexity Tuning for Hardware-Aware Probabilistic Circuits. 283-295 - Manuele Rusci, Marco Fariselli, Alessandro Capotondi, Luca Benini:
Leveraging Automated Mixed-Low-Precision Quantization for Tiny Edge Microcontrollers. 296-308
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