Zobrazeno 1 - 10
of 180
pro vyhledávání: '"Beigl, P."'
Earphones can give access to sensitive information via voice assistants which demands security methods that prevent unauthorized use. Therefore, we developed EarCapAuth, an authentication mechanism using 48 capacitive electrodes embedded into the sof
Externí odkaz:
http://arxiv.org/abs/2411.04657
Autor:
Lepold, Philipp, Röddiger, Tobias, King, Tobias, Kunze, Kai, Maurer, Christoph, Beigl, Michael
Publikováno v:
UbiComp '24: Companion of the 2024 on ACM International Joint Conference on Pervasive and Ubiquitous Computing, Pages 916 - 92
While traditional earphones primarily offer private audio spaces, so-called "earables" emerged to offer a variety of sensing capabilities. Pioneering platforms like OpenEarable have introduced novel sensing platforms targeted at the ears, incorporati
Externí odkaz:
http://arxiv.org/abs/2410.06533
In the realm of human activity recognition (HAR), the integration of explainable Artificial Intelligence (XAI) emerges as a critical necessity to elucidate the decision-making processes of complex models, fostering transparency and trust. Traditional
Externí odkaz:
http://arxiv.org/abs/2408.11552
Autor:
Dinh, Tu Anh, Mullov, Carlos, Bärmann, Leonard, Li, Zhaolin, Liu, Danni, Reiß, Simon, Lee, Jueun, Lerzer, Nathan, Ternava, Fabian, Gao, Jianfeng, Röddiger, Tobias, Waibel, Alexander, Asfour, Tamim, Beigl, Michael, Stiefelhagen, Rainer, Dachsbacher, Carsten, Böhm, Klemens, Niehues, Jan
With the rapid development of Large Language Models (LLMs), it is crucial to have benchmarks which can evaluate the ability of LLMs on different domains. One common use of LLMs is performing tasks on scientific topics, such as writing algorithms, que
Externí odkaz:
http://arxiv.org/abs/2406.10421
In recent years, deep learning has emerged as a potent tool across a multitude of domains, leading to a surge in research pertaining to its application in the wearable human activity recognition (WHAR) domain. Despite the rapid development, concerns
Externí odkaz:
http://arxiv.org/abs/2401.05477
Designing domain specific neural networks is a time-consuming, error-prone, and expensive task. Neural Architecture Search (NAS) exists to simplify domain-specific model development but there is a gap in the literature for time series classification
Externí odkaz:
http://arxiv.org/abs/2310.18384
Deep learning has proven to be an effective approach in the field of Human activity recognition (HAR), outperforming other architectures that require manual feature engineering. Despite recent advancements, challenges inherent to HAR data, such as no
Externí odkaz:
http://arxiv.org/abs/2307.07770
The vulnerability of the high-performance machine learning models implies a security risk in applications with real-world consequences. Research on adversarial attacks is beneficial in guiding the development of machine learning models on the one han
Externí odkaz:
http://arxiv.org/abs/2211.08384
Autor:
Huang, Yiran, Schaal, Nicole, Hefenbrock, Michael, Zhou, Yexu, Riedel, Till, Fang, Likun, Beigl, Michael
To this day, a variety of approaches for providing local interpretability of black-box machine learning models have been introduced. Unfortunately, all of these methods suffer from one or more of the following deficiencies: They are either difficult
Externí odkaz:
http://arxiv.org/abs/2201.01044
An essential task in predictive maintenance is the prediction of the Remaining Useful Life (RUL) through the analysis of multivariate time series. Using the sliding window method, Convolutional Neural Network (CNN) and conventional Recurrent Neural N
Externí odkaz:
http://arxiv.org/abs/2008.03961