The NorNet testbed (https://www.nntb.no) is an Internet testbed platform for research on multi-homed systems. The particular property of multi-homed systems is to be connected to multiple Internet Service Providers (ISP) simultaneously. Its initial purpose is of course to still provide connectivity in case of ISP/network failures. But does it really work that well, also with current protocols and applications? And redundancy does not come for free. A user connected to multiple ISPs will also receive multiple Internet bills each month. So, is there a possibility to make further use of multi-homing in the usual case where nothing goes wrong? Obviously, there are a lot of interesting research questions, which need to be examined in realistic Internet setups! Therefore, we are building up the NorNet open Internet testbed platform as a Linux- and Open-Source-software-based infrastructure, which currently spreads over multiple sites in different countries.

NorNet makes extensive use of advanced Linux features like Kernel-based Virtualisation (KVM), Linux Containers (LXC), BTRFS file system features, IP routing rules, Stream Control Transmission Protocol (SCTP), Multi-Path TCP (MPTCP), and many more. The goal of this talk is therefore to present an overview of the testbed, its underlying Linux features, and how they are combined to provide the multi-homing features to the various testbed users. This particularly includes an overview of how to make use of multi-path transport with MPTCP – based on the Linux MPTCP implementation – in multi-homed environments. The idea is to provide guidelines for also utilising multi-homing features in own projects.

This white paper on AI and ML as enablers of beyond 5G (B5G) networks is based on contributions from almost 20 5G PPP projects, coordinated through the 5G PPP Technology Board, that research, implement and validate 5G and B5G network systems. The paper introduces the main relevant mechanisms in Artificial Intelligence (AI) and Machine Learning (ML), currently investigated and exploited for enhancing 5G and B5G networks.

The holy grail in endoscopy examinations has for a long time been assisted diagnosis using Artificial Intelligence (AI). Recent developments in computer hardware are now enabling technology to equip clinicians with promising tools for computer-assisted diagnosis (CAD) systems. However, creating viable models or architectures, training them, and assessing their ability to diagnose at a human level, are complicated tasks. This is currently an active area of research, and many promising methods have been proposed. In this chapter, we give an overview of the topic. This includes a description of current medical challenges followed by a description of the most commonly used methods in the field. We also present example results from research targeting some of these challenges, and a discussion on open issues and ongoing work is provided. Hopefully, this will inspire and enable readers to future develop CAD systems for gastroenterology.