Accepted Workshops at IoT2020
Machine Learning (ML) is an area of research within Artificial Intelligence (AI) which uses complex mathematical models and data to recognize patterns and to aid decision making in different application areas. A key aspect of ML is the training when the machine is learning to detect patterns and make good classifications. While advances in the field have improved ML-methods, nonetheless humans also frequently have a crucial role to play in training of the models. In interactive machine learning (IML) the training occurs during an initial period and may also continuously occur based on user interaction. Yet how humans interact with ML models in training is not well understood. Connecting humans and ML as well as potentially massive datasets into interactive machine learning, we argue requires a multidisciplinary approach. This workshop aims to focus on humans interacting with models in training from the perspectives of HCI, Interaction Design, and Computer Science, by mapping UX and IML according to factors shared by both, with implications for future HCI research.
We invite researchers and practitioners in the areas of HCI, Interaction Design and Computer Science to apply for the workshop.
Please prepare a 3-4 page position paper in the ACM single-column Word Submission Template. Follow the instructions embedded in the template file for detailed guidance on applying the paragraph styles and including figures in your manuscript. Your manuscript will be produced in a single-column without any additional formatting. Then submit it using easychair submission page.
Position papers should be fitting within at least one of the following categories of submission:
- Practical case studies of Human-Machine Learning interaction (e.g. case studies related to specific application areas, related to different interaction modalities, related to device/object/app design features, ML experiments and simulations, etc.)
- Methodological exploration of understanding and designing Human-Machine Learning interaction. (e.g. user studies, system evaluations, Research through Design processes, etc.)
- Theoretical explorations of Human-Machine Learning interaction (e.g. system design models, critical analysis, cultural analysis, etc.)
- Demos and design implementations. Authors of the accepted position papers within this category will be asked to present their demos during the workshop.
Position papers will be peer-reviewed and the accepted position papers will be included in the companion proceedings and will be published in ACM digital library.
Maliheh Ghajargar (Malmö University, IoTaP research centre, Sweden)
Jan Persson (Malmö University, IoTaP research centre, Sweden)
Jeffrey Bardzell (Indiana University, Bloomington, USA)
Lars Holmberg (Malmö University, Sweden)
Agnes Tegen (Malmö University, IoTaP research centre, Sweden)
Deadline for submission: July 3, 2020
Mobile phones, smart watches, fitness trackers and a variety of Internet of Things (IoT) smart home devices constantly acquire data about our lives and our bodies. These data can be used to support healthcare and foster healthy and independent living, with potentially enormous benefits to our societies. Along with benefits, important challenges come in terms of quality and meaningfulness of the collected data, usability and long term acceptance, transparency, data protection and trustability. The aim of this workshop is to explore these fundamental questions with researchers and practitioners from academia and industry.
Dario Salvi (Malmö University, Sweden)
Francesco Potortí (CNR, Pisa, Italy)
Deadline for submission: July 15, 2020
Internet of Things for Emergency Management
The Internet of Things (IoT) has changed our approach to safety systems by connecting sensors and providing real-time data to managers and endangered people in emergencies, such as fires, earthquakes, floods, hurricanes, and even overcrowding. Furthermore, the recent situation regarding COVID-19 presents an unprecedented and extended emergency impacting humans’ social and economic life. IoTEM is a venue to gather researchers and practitioners who use information and communication technologies to address issues that happen before, during, and after an emergency.
The workshop covers all areas of emergency management that use the Internet of Things, cyber-physical systems, and pervasive computing. Topics of interest include, but are not limited to:
- IoT-based hardware and software infrastructures for emergency handling
- Sensing, networking, processing, and actuation for emergency management
- IoT-based collaborative working during COVID-19
- Social distancing systems for COVID-19
- Humans (people, emergency managers, developers, firefighters, etc) in IoT systems
- IoT-based emergency situational awareness
- Disaster communication infrastructures, technologies, and services
- IoT decision support technologies
- IoT-based microservices design for emergency
- IoT-related HCI for disaster preparation
- Self-adaptation and self-adaptive architectures for disaster
- Quality of Software-oriented disaster management IoT systems
- IoT and social-behavioral analysis of endangered people
- Big data processing for hazards
- IoT-robotics systems for disaster handling
- Remote sensing and cyber-physical systems
- Wireless networks based solutions for disaster management
- IoT-based modeling and simulation for crisis and disaster situations
- Prediction and early warning systems
- Crowd and queue management systems
- Emergency traffic management systems
- Real-world applications and field studies
- Indoor/outdoor emergency evacuation
Since the significant conferences of disaster management community (such as ISCRAM) are postponed or are being held far from Europe, we have the plan to advertise and attract the community (ISCRAM, ICT-DM, HICSS) to submit their work and participate in IoT2020 conference.
Henry Muccini (University of L’Aquila, Italy)
Julie Dugdale (University of Grenoble Alps, France)
Mahyar T. Moghaddam (University of L’Aquila, Italy – INRIA Grenoble, France)
Information on submission and deadline to be posted soon.
Tutorial: Linked Data Techniques for the Web of Things
The tutorial covers web technologies for specifying and executing applications involving networked sensors and actuators based on a logical representation of world state and application behaviour.
Interoperability is a major challenge on the Internet of Things. Different system vendors follow different paradigms in system architecture and data modelling, which makes the combination of heterogeneous devices into applications difficult. As web technologies have been designed for large-scale interoperability, both people from industry and academia have started to use web architecture to address the interoperability challenge on the Internet of Things.
In this tutorial, we cover light-weight semantic web technologies, commonly dubbed as Linked Data, for the Internet of Things community to fully exploit the standardisation efforts. We present the technology stack of Linked Data, address conceptual differences with other technology stacks for the Internet of Things, and show how to use Linked Data-based access to build applications on the Internet of Things.
Andreas Harth (University of Erlangen-Nuremberg and Fraunhofer IIS-SCS)
Tobias Käfer (Karlsruhe Institute of Technology )
Information on call for participation and deadline to be posted soon.