Carinthia is full of lakes and the AQUArinthia Amazon Alexa Skill allows you to get information about these lakes via your Amazon or Echo or Echo Dot.

You can use Alexa to check sea temperatures using "Alexa, ask Aquarinthia how warm the Wörthersee is" or "Alexa, ask Aquarinthia how high the level of the Keutschacher is".

To install the skill, use your Alexa Companion on your smartphone and search for "Aquarinthia" under Skills.

This skill is open source! The SourcCode of the current version can be found on GitHub -> https://github.com/salendron/aquarinthia-alexa-skill

All data in this app comes from the Open Government Data (OGD) portal of the state of Carinthia and is licensed under the Creative Commons Attribution 3.0 Austria License.

An annotated dataset of audio interactions with a conversational assistant with background sounds of 19 activities. Abstract: Conversational assistants in the form of stand-alone devices such as Amazon Echo and Google Home have become popular and embraced by millions of people. By serving as a natural interface to services ranging from home automation to media players, conversational assistants help people perform many tasks with ease, such as setting timers, playing music and managing to-do lists. While these systems offer useful capabilities, they are largely passive and unaware of the human behavioral context in which they are used. In this work, we explore how off-the-shelf conversational assistants can be enhanced with acoustic-based human activity recognition by leveraging the short interval after a voice command is given to the device. Since always-on audio recording can pose privacy concerns, our method is unique in that it does not require capturing and analyzing any audio other than the speech-based interactions between people and their conversational assistants. In particular, we leverage background environmental sounds present in these short duration voice-based interactions to recognize activities of daily living. We conducted a study with 14 participants in 3 different locations in their own homes. We showed that our method can recognize 19 different activities of daily living with average precision of 84.85% and average recall of 85.67% in a leave-one-participant-out performance evaluation with 30-second audio clips bound by the voice interactions. IRB approved under ID: 2016020035-MODCR01