About the project

Objective
The aim of this project is to analyse the environmental impacts of increased digitalization and the use of Information and Communication Technologies. The project can include both method development and case studies. The impacts will be analysed using life cycle assessment and life cycle thinking. Case studies can vary on different scales and include specific devices, applications and sectoral assessments. Initially, the focus will be on climate impacts and energy use, but it may also be broadened to a larger spectrum of environmental impacts. Assessments will include the direct impacts of ICT but also different types of indirect impacts, including rebound effects.

Background
The ICT sector has an environmental footprint. The future development of this footprint is debated, and it is important that the discussions have a scientific basis. Digitalisation may be a tool for reducing environmental impacts. By improving efficiencies and dematerialising products and services, new ICT applications can reduce the footprints of other sectors. More studies are, however, needed in order to understand when this actually leads to decreased impacts and when there is a risk for indirect rebound effects that increase use and footprints. Environmental life cycle assessment is a standardised method for assessing potential environmental impacts of products, services and functions “from the cradle to the grave”, i.e. from the extraction of raw materials via production and uses to waste management. It is used for analysing the environmental footprints, i.e. the direct impacts, of ICT. It can also be used for analysing different types of indirect effects.

Partner Postdocs
After working in the industry on large-scale refrigeration and heat pump systems and as an entrepreneur with solar pumps, Shoaib Azizi undertook a master’s program in Sustainable Energy Engineering at KTH. He moved to Umeå in northern Sweden for a multi-disciplinary PhD project on energy-efficient renovation of buildings. His PhD included research on the opportunities for digital tools to improve management and energy efficiency in buildings. He defended his thesis “A multi-method Assessment to Support Energy Efficiency Decisions in Existing Residential and Academic Buildings” in September 2021. Now Shoaib is a Digital Futures Postdoc researcher in digitalization and climate impacts at the Department of Sustainable Development, Environmental Science and Engineering (SEED) at KTH. His research involves lifecycle assessment methodology to understand various aspects of digitalization and its impacts on the environment.

Anna Furberg defended her PhD thesis in 2020 at Chalmers University of Technology. Her thesis, titled “Environmental, Resource and Health Assessments of Hard Materials and Material Substitution: The Cases of Cemented Carbide and Polycrystalline Diamond”, involved Life Cycle Assessment (LCA) case studies and method development. After her thesis, she worked at the Norwegian Institute for Sustainability Research, NORSUS, on various LCA projects and, in several cases, as the project leader. In 2022, she was awarded the SETAC Europe Young Scientist Life Cycle Assessment Award, which recognizes exceptional achievements by a young scientist in the field of LCA. Anna has a Digital Futures Postdoc position in digitalization and climate impacts at the Department of Sustainable Development, Environmental Science and Engineering (SEED) at KTH.

Supervisor
Göran Finnveden is a Professor of Environmental Strategic Analysis at the Department of Sustainable Development, Environmental Sciences and Engineering at KTH. He is also the director of the Mistra Sustainable Consumption research program. His research is focused on sustainable consumption and life cycle assessment, and other sustainability assessment tools. The research includes method development and case studies in different areas, including the environmental impacts of ICT.

About the project

Objective
The main objective of this project is to improve the productivity of two packaging lines within the SweOps Steriles function, as measured by Overall Equipment Effectiveness (OEE) and competence in line staffing. We aim to achieve the objective by enabling next-best-action decision support for front-line operators.

Background
Modern pharmaceutical packaging lines are complex systems with multiple intricate physical and digital components. Operators gain domain expertise through extended exposure and interaction with the systems. They see, touch, and listen to the operating parts of the system. With time, they develop deep procedural knowledge and reach the level where they can predict when the physical systems need maintenance. How do they do this? This is the basic question of the project SMART: Smart Predictive Maintenance for the Pharmaceutical Industry, a collaboration between AstraZeneca and KTH.

Our approach deploys three pillars: 1) sensor networks in manufacturing, 2) machine learning predictive models, and 3) interactive immersive and contextual visualizations. We will observe and interview expert operators to acquire their procedural knowledge and focus on the sensing and machine learning tools to produce a rich sensor-based predictive model that we visualize peripherally in the plant and immersively to the operators. The project aims to enhance the operators’ abilities to perform predictive maintenance and expedite the transfer of these skills to novice operators via novel digital tools.

Partner Postdoc
Tianzhi Li

Main supervisor
Lihui Wang, Professor and Chair of Sustainable Manufacturing at KTH

Co-supervisors
Jan Kronqvist, Assistant Professor at KTH
Ming Xiao, Associate Professor, Division of ISE at KTH EECS
Mario Romero, Associate professor at KTH EECS School, Division of Computational Science and Technology

Watch the recorded presentation at the Digitalize in Stockholm 2023 event:

Background and summary of fellowship
Behaviour Trees (BTs) represent a hierarchical way of combining low-level controllers for different tasks into high-level controllers for more complex tasks. The key advantages of BTs have been shown to include the following:

• Recursive structure: The BT is a rooted tree and at every edge of that tree, the interface between the parent and the subtree is the same, centred around a return status of either Success, Failure or Running.
• Modularity: Due to the recursive structure, a complex subtree can be seen as a single leaf, and vice versa. This enables the encapsulation of complexity.
• Transparency: The recursive structure of BTs makes them human-readable. You can always look at a BT and see why it is executing a particular behaviour. This fact in combination with the modularity described above enables a user to understand complex BTs by analyzing one subtree at a time.
• An efficient tool for human system design: BTs were created by computer game programmers to make their life easier when creating complex AI designs. Modularity is a well-known tool to handle complexity, and transparency is vital in any human design.
• An efficient tool for automated design. The modular recursive structure simplifies automated design.
• A structure that enables formal analysis of safety and convergence. Formal analysis of convergence and region of attraction is enabled by the modular recursive structure.

In this project, we will use the properties of BTs listed above to synthesize controllers that combine the efficiency of reinforcement learning with formal performance guarantees such as safety and convergence to a designated goal area.

Background and summary of fellowship
Reinforcement Learning (RL) is concerned with learning efficient control policies for systems with unknown dynamics and reward functions. RL plays an increasingly important role in a large spectrum of application domains including online platforms (recommender systems and search engines), robotics, and self-driving vehicles. Over the last decade, RL algorithms, combined with modern function approximators such as deep neural networks, have shown unprecedented performance and have been able to solve very complex sequential decision tasks better than humans. Yet, these algorithms are lacking robustness, and are most often extremely data inefficient.

This research project aims at contributing to the theoretical foundations for the design of data-efficient and robust RL algorithms. To this aim, we develop a fundamental two-step process:

1. We characterize information-theoretical limits for the performance of RL algorithms (in terms of sample complexity, i.e., data efficiency)
2. We leverage these limits to guide the design of optimal RL algorithms, algorithms approaching the fundamental performance limits

Background and summary of fellowship
In many application areas, it is not sufficient to present the output of machine learning models to the users without providing any information on what leads to the specific predictions or recommendations and how (un)certain they are. The strongest machine learning models are however often essentially black boxes. In order to enable trust in such models, techniques for explaining the predictions in the form of interpretable approximations are currently being investigated. Another cornerstone for enabling trust is that the uncertainty of the output of the machine learning models is properly quantified, e.g., that the output prediction intervals or probability distributions are well-calibrated.

Motivated by collaborations with Karolinska Institutet/University hospital on sepsis prediction, Scania on predictive maintenance and the Swedish National Financial Management Authority on gross domestic product (GDP) forecasting, techniques for quantifying uncertainty and explaining predictions will be developed and evaluated. In addition to scientific papers, the output of the project will be Python packages to support reliable machine learning, enabling predictions of state-of-the-art machine learning models to be complemented with explanations and uncertainty quantification.

Background and summary of fellowship
Social robots and virtual agents are currently being explored and developed for applications in a number of fields such as education, service, retail, health, elderly care, simulation and training and entertainment. For these systems to be accepted and successful, not only in task-based interaction but also to maintain user engagement, in the long run, it is important that they can exhibit varied and meaningful non-verbal behaviours, and also possess the ability to adapt to the interlocutor in different ways. Adaptivity in face-to-face interaction (sometimes called mimicry) has for example been shown to increase liking and affiliation.

This work addresses how style aspects in non-verbal interaction can be controlled, varied and adapted, across several modalities including speech, gesture and facial expression. The project entails novel data collection of verbal and non-verbal behaviours (audio, video, gaze tracking and motion capture) with rich style variation, but also makes use of existing datasets for base training. Synthesis models trained on this data will be based primarily on deep probabilistic generative modelling, conditioned with relevant style-related parameters. Multimodal generation paradigms, that produce congruent behaviours in more than one modality at a time, e.g. both speech and gesture, in a coherent style, will also be explored and evaluated in perceptual studies or experiments with real interactive contexts.

About the project

Objective
The objective of the Swedish Question Generation for Assessing Reading Comprehension (SWE-QUEST) project is to develop a demo system that, given a text, automatically generates multiple-choice reading comprehension questions on the text, as shown in the picture below.

Using a neural-network-based generative model, the demo system will generate the whole multiple-choice question, both the question itself and the answer alternatives. Although the example above was in English, our demo system will work for Swedish text. The project will push the state-of-the-art in natural language generation. The system is intended to be used by teachers of SFI (Svenska för invandrare) to facilitate test construction and the development of teaching materials. Still, it can also be used for self-studies of Swedish.

Background
The SFI students form a heterogeneous group, and the SFI classes are often too big to allow the teacher time to adapt the level of teaching to cater to each student individually. Therefore, developing such a tool could be highly useful. It would rapidly and easily generate several suggested multiple-choice questions (MCQ) on text material for teaching and assessment.

The task of automatically generating reading comprehension questions (without distractors) using neural methods has been studied before, primarily for English. There have also been some attempts at generating distractors, given the question and the correct answer, using neural methods. However, no attempt has been made to construct a trainable model that generates the whole MCQ in one go.

Crossdisciplinary collaboration
The researchers in the team represent the KTH Schools of Electrical Engineering and Computer Science, the Division of Speech, Music and Hearing and the Department of Swedish Language and Multilingualism at Stockholm University.

Watch the recorded presentation at the Digitalize in Stockholm 2023 event: