If you are interested in learning how to create intelligent environments that can interact with users and enrich their activities, then you should join the IE2025 tutorials on intelligent environments. These tutorials will provide you with an overview of the main concepts, challenges, and opportunities in this multidisciplinary field. You will learn about state-of-the-art research and applications in areas such as sensors and actuators, the Internet of Things, signal processing (incl. audio and images), context awareness, artificial intelligence, human-computer interaction, software engineering, and pervasive and ubiquitous computing. You will also have the chance to interact with experts and peers and to get hands-on experience with some of the tools and platforms for developing intelligent environments.
All tutorials can be attended in person at the conference site or remotely. They will be given in person, hybrid (some lecturers onsite and others presenting remotely), or online.
Which Missing Feature Should I Acquire If My Data Is Streaming? – An Introduction to Active Feature Acquisition on Data Streams
In-person
In machine learning, missing features can disrupt predictive models. Active Feature Acquisition (AFA) addresses this by identifying which missing data to acquire under budget constraints. This tutorial introduces AFA for static and streaming data, guiding participants through implementing stream-based AFA methods. By the end, participants will have hands-on experience developing their own AFA solutions. This tutorial is designed for attendees unfamiliar with the topic but will also provide valuable insights for researchers working with data streams.
Intended audience: This tutorial is designed for beginners in machine learning who are looking to expand their knowledge of handling missing data, particularly in real-time data streams. It is also relevant for researchers in data stream processing who wish to explore techniques for acquiring missing features under resource constraints. No prior experience with Active Feature Acquisition (AFA) is required, but a basic understanding of machine learning concepts will be helpful.
Additional Information: Participants are encouraged to bring laptops with Python pre-installed, as coding exercises will be done in Python. Further instructions on software setup will be provided prior to the tutorial.
– Christian Beyer, M.Sc. in Computer Science, Otto-von-Guericke University Magdeburg
Introducing a Quality Enhanced Process for Developing Intelligent Environments
In-person
The development of Intelligent Environments presents significant complexity due to the diverse and advanced technologies involved. These systems integrate a range of components, including Sensor Networks, Artificial Intelligence algorithms, and user-personalised Human-Computer Interfaces. Each of these technologies is intricate on its own, and their combination in a cohesive system adds further layers of complexity. Additionally, there is no standard accepted way to build them which will guarantee good outcomes.
The aim of this tutorial is to introduce an enhanced approach to the User-Centred Intelligent Environment Development Process, referred to as UCIEDP2. This refined process aims to improve the development lifecycle of Intelligent Environment systems by emphasising quality assurance at every stage. UCIEDP2 focuses on integrating best practices, structured methodologies, and rigorous evaluation criteria to address the challenges associated with developing these complex systems. By providing a more systematic framework, UCIEDP2 seeks to ensure that the resulting Intelligent Environments are not only technically sound but also ethically viable for their intended users.
Intended audience: The tutorial will be designed to be accessible to a broad audience. However, attendees with a background in computer science, engineering, or software engineering will find it easier to grasp the concepts presented.
Additional Information: Participants are encouraged to bring laptops with Python pre-installed, as coding exercises will be done in Python. Further instructions on software setup will be provided prior to the tutorial.
– Aditya Santokhee, Middlesex University Mauritius
Tutorial on Explainable and Robust AI for Industry 4.0 & 5.0 (X-RAI)
In-person
Ensuring the robustness and providing explanations for Artificial Intelligence (AI) solutions applied in the industry is imperative, particularly in the context of securing trustworthy AI. Increasingly complex real-world applications of AI utilise black-box models based on deep learning approaches to demonstrate high predictive accuracy and enhance industrial process efficiency. However, the decisions made by these black-box models are often difficult for human experts to understand – and, consequently, to act upon. The complete action plan to be performed based on, for example, the detected symptoms of damage and wear often requires complex reasoning and planning processes, involving many actors and balancing different priorities. Thus, operators, technicians and managers require insights to understand what is happening, why it is happening, what is the uncertainty in the observation, and how to react. The effectiveness of an industrial system hinges on the relevance of the actions undertaken by operators in response to the alarms. Therefore, establishing trustworthy AI involves not only accurate detection but also the provision of understandable, reliable, and comprehensive insights to facilitate informed decision-making and enhance the overall performance and robustness of the industrial system, which can be demonstrated by the adaptiveness and efficient decision-making in complex and fast-changing environments.
Intended audience: Researchers interested in using AI/ML to solve realistic industrial problems, or in understanding the challenges in applying AI/ML methods to industrial problems, with XAI methods to provide accountability, causality, explainability, fairness, interpretability, privacy, robustness, and transparency. We expect the tutorial to attract researchers from academia and different sectors of the industry. This tutorial will benefit researchers and students who would like an introduction to explainable AI and robustness. It will also be of interest to researchers who have been working on IoT and industrial data mining applications. Participants are expected to have basic knowledge of data science and machine learning.
– Sepideh Pashami, Halmstad University, Sweden
– Grzegorz J. Nalepa, Jagiellonian University, Krakow, Poland
– Joao Gama, University of Porto, Porto, Portugal