Explanations of news personalisation across countries and media types

News outlets worldwide increasingly adopt user- and system-driven personalisation to individualise their news delivery. Yet, the technical implementation of news personalisation systems, in particular the one relying on algorithmic news recommenders (ANRs) and tailoring individual news suggestions with the help of user data, often remains opaque. In our article, we examine how news personalisation is used by quality and popular media in three countries with different media accountability infrastructures - Brazil, the Netherlands, and Russia - and investigate how information about personalisation usage is communicated to the news readers via privacy policies. Our findings point out that news personalisation systems are predominantly treated as black boxes that indicate a significant gap between practice and theory of algorithmic transparency, in particular in the non-EU context

Topological Graph Neural Networks

Graph neural networks (GNNs) are a powerful architecture for tackling graph learning tasks, yet have been shown to be oblivious to eminent substructures, such as cycles. We present TOGL, a novel layer that incorporates global topological information of a graph using persistent homology. TOGL can be easily integrated into any type of GNN and is strictly more expressive in terms of the Weisfeiler--Lehman test of isomorphism. Augmenting GNNs with our layer leads to beneficial predictive performance for graph and node classification tasks, both on synthetic data sets, which can be classified by humans using their topology but not by ordinary GNNs, and on real-world data

Audio-based Roughness Sensing and Tactile Feedback for Haptic Perception in Telepresence

Haptic perception is highly important for immersive teleoperation of robots, especially for accomplishing manipulation tasks. We propose a low-cost haptic sensing and rendering system, which is capable of detecting and displaying surface roughness. As the robot fingertip moves across a surface of interest, two microphones capture sound coupled directly through the fingertip and through the air, respectively. A learning-based detector system analyzes the data in real time and gives roughness estimates with both high temporal resolution and low latency. Finally, an audio-based vibrational actuator displays the result to the human operator. We demonstrate the effectiveness of our system through lab experiments and our winning entry in the ANA Avatar XPRIZE competition finals, where briefly trained judges solved a roughness-based selection task even without additional vision feedback. We publish our dataset used for training and evaluation together with our trained models to enable reproducibility of results.Comment: IEEE International Conference on Systems, Man, and Cybernetics (SMC), Honolulu, Hawaii, USA, October 202

Robust Immersive Telepresence and Mobile Telemanipulation: NimbRo wins ANA Avatar XPRIZE Finals

Robotic avatar systems promise to bridge distances and reduce the need for travel. We present the updated NimbRo avatar system, winner of the $5M grand prize at the international ANA Avatar XPRIZE competition, which required participants to build intuitive and immersive robotic telepresence systems that could be operated by briefly trained operators. We describe key improvements for the finals, compared to the system used in the semifinals: To operate without a power- and communications tether, we integrated a battery and a robust redundant wireless communication system. Video and audio data are compressed using low-latency HEVC and Opus codecs. We propose a new locomotion control device with tunable resistance force. To increase flexibility, the robot's upper-body height can be adjusted by the operator. We describe essential monitoring and robustness tools which enabled the success at the competition. Finally, we analyze our performance at the competition finals and discuss lessons learned.Comment: M. Schwarz and C. Lenz contributed equall