Knowledge Graph Question Answering using Graph-Pattern Isomorphism - Vollmers, Daniel; Jalota, Rricha; Moussallem, Diego; Topiwala, Hardik; Ngonga Ngomo, Axel-Cyrille; Usbeck, Ricardo (CoRR)

MLCheck- Property-Driven Testing of Machine Learning Models - Sharma, Arnab; Demir, Caglar; Ngonga Ngomo, Axel-Cyrille; Wehrheim, Heike (CoRR)

Open Data Portal Germany (OPAL) Projektergebnisse - Wilke, Adrian; Ngonga Ngomo, Axel-Cyrille (CoRR)

Out-of-Vocabulary Entities in Link Prediction - Demir, Caglar; Ngonga Ngomo, Axel-Cyrille (CoRR)

DRILL- Deep Reinforcement Learning for Refinement Operators in ALC - Demir, Caglar; Ngonga Ngomo, Axel-Cyrille (CoRR)

Trust Me, I’m Confident – Are Confident Members of the Crowd Better at Evaluating Business Model Ideas? - Laux, Florian; Görzen, Thomas (Proceedings of the 42nd International Conference on Information Systems (ICIS))

Zinc Anodizing: Structural Diversity of Anodic Zinc Oxide Controlled by the Type of Electrolyte - Engelkemeier, Katja; Sun, Aijia; Voswinkel, Dietrich; Grydin, Olexandr; Schaper, Mirko; Bremser, Wolfgang (ChemElectroChem)

Reinforcement Learning for Admission Control in Wireless Virtual Network Embedding - Afifi, Haitham; Sauer, Fabian Jakob; Karl, Holger (2021 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS) (ANTS'21))
Using Service Function Chaining (SFC) in wireless networks became popular in many domains like networking and multimedia. It relies on allocating network resources to incoming SFCs requests, via a Virtual Network Embedding (VNE) algorithm, so that it optimizes the performance of the SFC. When the load of incoming requests -- competing for the limited network resources -- increases, it becomes challenging to decide which requests should be admitted and which one should be rejected. In this work, we propose a deep Reinforcement learning (RL) solution that can learn the admission policy for different dependencies, such as the service lifetime and the priority of incoming requests. We compare the deep RL solution to a first-come-first-serve baseline that admits a request whenever there are available resources. We show that deep RL outperforms the baseline and provides higher acceptance rate with low rejections even when there are enough resources.
Keywords: reinforcement learning; admission control; wireless sensor networks

Reinforcement Learning-based Microphone Selection in Wireless Acoustic Sensor Networks considering Network and Acoustic Utilities - Afifi, Haitham; Guenther, Michael; Brendel, Andreas; Karl, Holger; Kellermann, Walter (14. ITG Conference on Speech Communication (ITG 2021))
Wireless Acoustic Sensor Networks (WASNs) have a wide range of audio signal processing applications. Due to the spatial diversity of the microphone and their relative position to the acoustic source, not all microphones are equally useful for subsequent audio signal processing tasks, nor do they all have the same wireless data transmission rates. Hence, a central task in WASNs is to balance a microphone’s estimated acoustic utility against its transmission delay, selecting a best-possible subset of microphones to record audio signals. In this work, we use reinforcement learning to decide if a microphone should be used or switched off to maximize the acoustic quality at low transmission delays, while minimizing switching frequency. In experiments with moving sources in a simulated acoustic environment, our method outperforms naive baseline comparisons
Keywords: microphone utility; microphone selection; wireless acoustic sensor network; network delay; reinforcement learning

Network-Aware Optimal Microphone Channel Selection in Wireless Acoustic Sensor Networks - Gunther, Michael; Afifi, Haitham; Brendel, Andreas; Karl, Holger; Kellermann, Walter (ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP))