Tutorials Tutorials at UbiComp/ISWC 2019

Tutorials Schedule

1. Modeling Human Behavior via Inverse Reinforcement Learning (Day 1, morning)

Organizers: Nikola Banovic

This tutorial will review current computational approaches to describe, simulate, and predict human behavior from empirical behavior traces data stored in large behavior logs. Attendees will learn how train computational models of human behavior using Inverse Reinforcement Learning and leverage them to create user interfaces that can automatically reason about and act in response to people’s behaviors. The tutorial will focus on high-level behaviors, such as routines and habits, represented by sequences of situations people find themselves in and actions they perform in those situations. After completing this tutorial, attendees will be able to formulate behavior modeling problems as computational modeling problems and to leverage such models to create User Interfaces that help people increase their productivity, safety, and health.

2. Data Visualization for UbiComp and ISWC Research (Day 1, afternoon)

Organizers: Eun Kyoung Choe (University of Maryland, USA), Petra Isenberg (Inria, France), Bongshin Lee (Microsoft Research Redmond, USA)

This half-day tutorial will cover an introductory level of visualization fundamentals and techniques needed to design and evaluate visualization in the context of ubiquitous computing research. It will focus on data visualization for mobile devices, such as smartwatches, smartphones, and tablets as well as time-oriented datasets. This introductory tutorial is targeted for UbiComp/ISWC researchers who want to learn how to effectively present data leveraging visualization. No specific prior knowledge or skills in computer science or visualization are required.

3. Smartphone App Usage, Understanding, Modelling, and Prediction (Day 2, morning)

Organizers: Yong Li (Tsinghua University, China), Vassilis Kostakos (University of Melbourne, Australia), Sha Zhao (Zhejiang University, Hangzhou, China), Sasu Tarkoma (University of Helsinki, Finland)

The wide adoption of mobile devices and smartphone applications (apps) has enabled highly convenient and ubiquitous access to Internet services. For both app developers and service providers, it becomes increasingly important to predict how users use mobile apps under various contexts. Mining and learning from smartphone apps for users is very relevant to ubiquitous computing. Smartphone app are ubiquitous in our daily life. Abundant apps provide useful services in almost every aspect of modern life. Easy to download and often free, apps can be fun and convenient for playing games, getting turn-by-turn directions, and accessing news, books, weather, and more. Apps on smartphones can be considered as the entry point to access everyday life services such as communication, shopping, navigation, and entertainment. Since a smartphone is linked to an individual user, apps in smartphones can sense users’ behavior and activities. Researchers use the data recorded by smartphone apps to analyze apps and understand users. However, there are still not yet so many researchers in this emerging field. One reason is the dataset available for research and the second reason is the area is relatively new. As the researchers working in this area with more than 5 years and publishing more than 30 related high quality papers in this area in the top conference, we would like to provide a tutorial will provide a compact platform to help researchers to focus on this research area, and also open several dataset in this tutorial.

This tutorial is helpful for researchers to learn the basic idea and techniques in this area, and also distribute the latest progress on this hop topic, to promote the research area. First, the participants of this workshop can learn the data, methods, tools, and experiences from the analysis of smartphone apps. Second, the findings and discussions that are presented in the tutorial can motivate researchers. Third, by bringing together participants with a variety of backgrounds and goals, this tutorial provides a platform for interdisciplinary cooperation and networking.

4. Eyewear Computing in the Wild

Organizers: George Chernyshov (KMD, Japan), Shoya Ishimaru (DFKI Kaiserslautern, Germany), Benjamin Tag (University Melbourne, Australia), Philipp M. Scholl (University Freiburg, Germany), Yuji Uema (JINS, Japan), Kai Kunze (KMD, Japan), Jamie A Ward (Goldsmith, UK)

Most of our senses involve the head, making it one of the most interesting body locations for the simultaneous sensing and interaction. Smart glasses, head-worn eye trackers, and similar “smart eyewear” have recently emerged as interesting research platform for ubiquitous computing. However, there are still very few open tools and too little open datasets perform this research. In the spirit of UbiComp, this tutorial will fill the gap, we plan a large scale data recording during the UbiComp main conference using Jins MEME, smart EOG enabled glasses. In this tutorial we present the Smart Eyewear toolchain consisting of smart glasses prototypes and a software platform for cognitive and social interaction assessments in the wild, with several application cases and a demonstration of activity recognition in real-time.

The tutorial participants should be ready to record during the main conference. They will get a set of glasses and a mobile phone. Participants recording data during the conference will have privileged access to the gathered data (after ca. ½ - 1 year the dataset will be made publically available).

Our platform is designed to work with Jins MEME, smart EOG enabled glasses. The user software is capable of data logging, posture tracking and recognition of several activities, such as talking, reading and blinking. Organizers will walk through several applications and studies that the platform has been used for.