This is a software artifact for the paper VAMOS: Middleware for Best-Effort Third-Party Monitoring by Marek Chalupa, Fabian Muehlboeck, Stefanie Muroya Lei, and Thomas A. Henzinger. This artifact extends the version of the artifact published for FASE 2023 which can be found at https://doi.org/10.5281/zenodo.7574688 (Version 1.0.0). The extension covers the new case study on monitoring integrity of Wayland connections. The old experiments can be reproduced with the Version 1.0.0 of the artifact (https://doi.org/10.5281/zenodo.7574688). The new experiments can be reproduced by using the new docker image vamos-sttt-wayland.tar.gz. First, load the image: docker load < vamos-sttt-wayland.tar.gz Then, identify what device is your touchpad or mouse by calling either evemu-describe or libinput list-devices. You will get a list of devices including information about the block file called /dev/input/eventXX where XX is a number. This is the file that we need. To make sure you are working with the right device, you try running evemu-record /dev/input/eventXX If you see events generated while using the device, it is the right one. Once you know the device, run: DEVTYPE=touchpad # set to "mouse" if your device is a mouse and not touchpadDEVICE=/dev/input/eventXX # replace with your touchpad/mouse device docker run --rm -it --network host -v /tmp/.X11-unix:/tmp/.X11-unix --env DISPLAY=$DISPLAY --env XDG_RUNTIME_DIR=/tmp --device /dev/dri/card1 --device $DEVICE --env XAUTH="$(xauth list|grep $(uname -n))" --env DEVTYPE=$DEVTYPE vamos:wayland /bin/bash Once you are in the shell inside docker, run ./experiments.sh More detailed README is attached. The source code of the first artifact can be found at https://github.com/ista-vamos/fase23-experiments and the source code of the new part of the artifact at at https://github.com/ista-vamos/sttt-experiments. The newest source code of VAMOS is on Github in the repository https://github.com/ista-vamos/vamos. Abstract of the paper: As the complexity and criticality of software increase every year, so does the importance of run-time monitoring. Third-party monitoring, with limited knowledge of the monitored software, and best-effort monitoring, which keeps pace with the monitored software, are especially valuable, yet underexplored areas of run-time monitoring. Most existing monitoring frameworks do not support their combination because they either require access to the monitored code for instrumentation purposes or the processing of all observed events, or both.We present a middleware framework, VAMOS, for the run-time monitoring of software which is explicitly designed to support third-party and best-effort scenarios. The design goals of VAMOS are (i) efficiency (keeping pace at low overhead), (ii) flexibility (the ability to monitor black-box code through a variety of different event channels, and the connectability to monitors written in different specification languages), and (iii) ease-of-use. To achieve its goals, VAMOS combines aspects of event broker and event recognition systems with aspects of stream processing systems. We implemented a prototype toolchain for VAMOS and conducted experiments including a case study of monitoring for data races. The results indicate that VAMOS enables writing useful yet efficient monitors, is compatible with a variety of event sources and monitor specifications, and simplifies key aspects of setting up a monitoring system from scratch.