Data protection in AI services: A survey

C Meurisch, M Mühlhäuser - ACM Computing Surveys (CSUR), 2021 - dl.acm.org
Advances in artificial intelligence (AI) have shaped today's user services, enabling
enhanced personalization and better support. As such AI-based services inevitably require …

Exploring user expectations of proactive AI systems

C Meurisch, CA Mihale-Wilson, A Hawlitschek… - Proceedings of the …, 2020 - dl.acm.org
Recent advances in artificial intelligence (AI) enabled digital assistants to evolve towards
proactive user support. However, expectations as to when and to what extent assistants …

The integrated sensing and communication revolution for 6G: Vision, techniques, and applications

N González-Prelcic, MF Keskin… - Proceedings of the …, 2024 - ieeexplore.ieee.org
Future wireless networks will integrate sensing, learning, and communication to provide new
services beyond communication and to become more resilient. Sensors at the network …

Service placement for collaborative edge applications

L Wang, L Jiao, T He, J Li, H Bal - IEEE/ACM Transactions on …, 2020 - ieeexplore.ieee.org
Edge computing is emerging as a promising computing paradigm for supporting next-
generation applications that rely on low-latency network connections in the Internet-of …

Unsupervised 4d lidar moving object segmentation in stationary settings with multivariate occupancy time series

T Kreutz, M Mühlhäuser… - Proceedings of the IEEE …, 2023 - openaccess.thecvf.com
In this work, we address the problem of unsupervised moving object segmentation (MOS) in
4D LiDAR data recorded from a stationary sensor, where no ground truth annotations are …

Privacy-preserving AI services through data decentralization

C Meurisch, B Bayrak, M Mühlhäuser - Proceedings of The Web …, 2020 - dl.acm.org
User services increasingly base their actions on AI models, eg, to offer personalized and
proactive support. However, the underlying AI algorithms require a continuous stream of …

LiOn-XA: Unsupervised Domain Adaptation via LiDAR-Only Cross-Modal Adversarial Training

T Kreutz, J Lemke, M Mühlhäuser… - 2024 IEEE/RSJ …, 2024 - ieeexplore.ieee.org
In this paper, we propose LiOn-XA, an unsupervised domain adaptation (UDA) approach
that combines LiDAR-Only Cross-Modal (X) learning with Adversarial training for 3D LiDAR …

SaS: SSD as SQL database system

JH Park, S Choi, G Oh, SW Lee - Proceedings of the VLDB Endowment, 2021 - dl.acm.org
Every database engine runs on top of an operating system in the host, strictly separated with
the storage. This more-than-half-century-old IHDE (In-Host-Database-Engine) architecture …

CoEdge: A Cooperative Edge System for Distributed Real-Time Deep Learning Tasks

Z Jiang, N Ling, X Huang, S Shi, C Wu, X Zhao… - Proceedings of the …, 2023 - dl.acm.org
Recent years have witnessed the emergence of a new class of cooperative edge systems in
which a large number of edge nodes can collaborate through local peer-to-peer …

Privacy-Preserving Artificial Intelligence on Edge Devices: A Homomorphic Encryption Approach

MJ Khan, B Fang, G Cimino, S Cirillo… - … Conference on Web …, 2024 - ieeexplore.ieee.org
Recent advancements in privacy-preserving artificial intelligence (AI) have paved the way
for enhanced privacy in computational processes. A standing challenge, however, is the …