10 citations · 21 across the 9 of their papers we have counts for
9 papers
Differential Privacy for Protecting Private Patterns in Data Streams
He Gu, Thomas Plagemann, Maik Benndorf +2
Complex event processing (CEP) is a powerful and increasingly more important tool to analyse data streams for Internet of Things (IoT) applications. These data streams often contai…
Towards Privacy Engineering for Real-Time Analytics in the Human-Centered Internet of Things
Thomas Plagemann, Vera Goebel, Matthias Hollick +1
Big data applications offer smart solutions to many urgent societal challenges, such as health care, traffic coordination, energy management, etc. The basic premise for these appli…
To Migrate or not to Migrate: An Analysis of Operator Migration in Distributed Stream Processing
Espen Volnes, Thomas Plagemann, Vera Goebel
One of the most important issues in data stream processing systems is to use operator migration to handle highly variable workloads in a cost-efficient manner and adapt to the need…
A Clinical Evaluation of a Low-Cost Strain Gauge Respiration Belt and Machine Learning to Detect Sleep Apnea
Stein Kristiansen, Konstantinos Nikolaidis, Thomas Plagemann +11
Sleep apnea is a serious and severely under-diagnosed sleep-related respiration disorder characterized by repeated disrupted breathing events during sleep. It is diagnosed via poly…
My Health Sensor, my Classifier: Adapting a Trained Classifier to Unlabeled End-User Data
Konstantinos Nikolaidis, Stein Kristiansen, Thomas Plagemann +8
In this work, we present an approach for unsupervised domain adaptation (DA) with the constraint, that the labeled source data are not directly available, and instead only access t…
Using Under-trained Deep Ensembles to Learn Under Extreme Label Noise
Konstantinos Nikolaidis, Thomas Plagemann, Stein Kristiansen +2
Improper or erroneous labelling can pose a hindrance to reliable generalization for supervised learning. This can have negative consequences, especially for critical fields such as…
Learning Realistic Patterns from Unrealistic Stimuli: Generalization and Data Anonymization
Konstantinos Nikolaidis, Stein Kristiansen, Thomas Plagemann +8
Good training data is a prerequisite to develop useful ML applications. However, in many domains existing data sets cannot be shared due to privacy regulations (e.g., from medical…
Augmenting Physiological Time Series Data: A Case Study for Sleep Apnea Detection
Konstantinos Nikolaidis, Stein Kristiansen, Vera Goebel +3
Supervised machine learning applications in the health domain often face the problem of insufficient training datasets. The quantity of labelled data is small due to privacy concer…
Learning from Higher-Layer Feature Visualizations
Konstantinos Nikolaidis, Stein Kristiansen, Vera Goebel +1
Driven by the goal to enable sleep apnea monitoring and machine learning-based detection at home with small mobile devices, we investigate whether interpretation-based indirect kno…