PIs: Emil Björnson, LIU, Pontus Giselsson, LU

PIs: David Broman, KTH, Magnus O. Myreen, CTH

PIs: Iolanda Leite, KTH, Jana Tumova, KTH

PIs: Benoit Baudry, KTH, Erik Elmroth, UMU

PIs: Bo Bernhardsson, LU, Maria Sandsten, LU

PIs: Giuseppe Durisi, CTH, Katerina Mitrokotsa, CTH

PIs: Anders Hansson, LIU, Bo Wahlberg, KTH

Objective

Improve the security of existing machine learning algorithms against real-world attackers

Targeted Problem

Data poisoning attacks on optimization-based supervised learning algorithms and their defense.

Approach

• Understand vulnerabilities of supervised learning algorithms. We will find blind spots of a broad family of supervised learning algorithms.
• Design optimal defense strategies against data poisoning attacks. We will develop two defense frameworks: The detection framework aims to accurately and timely detect the occurrence of poisoning attacks and the mitigation framework aims to effectively reduce the influence of attacks on victim learning models.
• Improve solution robustness and evaluate proposed methods. We will refine the detection and mitigation frameworks and propose new algorithms to compute optimal defense. We will design evaluation frameworks and validate solutions on real-world data sets

PIs

Bo An (NTU), Chew Lock Yue (NTU), Christos Dimitrakakis (Chalmers), Devdatt Dubhashi (Chalmers)

Objective

To develop co-evolutionary algorithms for reinforcement learning in multi-agent systems

Approach

• Demonstrate the techniques on adaptive scheduling of an intelligent autonomous bus network at NTU campus

Preliminary Results

• Proof of concept for elevator scheduling (Figure 1)
• Data analytics on campus bus network (Figure 2)
• Preliminary results in simulations (Figure 3)

PIs

Chew Lock-Yue (NTU), Bo An (NTU), Mikael Johasson (KTH)

Objective

Interactive training of deep networks for vision-based autonomous systems. Reproducible machine learning of navigation and path following in autonomous vehicles such as cars and drones

Targeted Problem

Generate learning data in an interactive, dynamic, and adaptive way.

Approach

• Virtual paths, i.e., synthetic environments are projected onto the ground.
• Small-scale vehicles drive autonomously in these synthetic scenarios and acquire their training data in a dynamic environment that can adapt to the learning progress.
• Allowing for full control of all environmental parameters, for appropriate
• Generation of training data by photo realistic rendering of virtual images during real navigation and path following

PIs

Michael Felsberg (LiU), Kai-Kuang Ma (NTU)

Objective

Computing capacity in edge locations and the wireless access are managed separately. However, delivering the full potential of MECs requires that edge locations and wireless networks be managed in concert

Targeted Problem

To design new resource allocation methods to improve and unify the management of Mobile Edge Clouds (MECs).

Approach

• Game theory will be used to analyze the optimal strategy of each player, thus verifying that the goals of the devised mechanisms are met.
• A player interaction protocol will devised and validated using simulations.
• Development of mathematical models and algorithms to efficiently solve the network and computing resource allocation for MECs.

PIs

Erik Elmoroth (UmU), Kai-Kuang Ma (NTU)

Objective

Improved algorithms for sensor fusion, localization and coordination for search and rescue operations and surveillance applications

Targeted Problem

Centralized and distributed sensor fusion for target localization, reliable relative localization and collaborative control and task planning of the agents

Approach

• Environmental modelling of stationary and moving targets.
• Centralized and distributed fusion estimation methods for multiple robots.
• Reliable relative localization in GPS denied environments.
• Distributed optimization and hybrid control from local temporal logic specifications for multi-robot systems

PIs

Fredrik Gustafsson (LiU), Dimos Dimarogonas (KTH), Hu Guoqiang (NTU)

Objective

Develop advanced techniques and tools for visualizing different aspects of machine learning jointly in the same framework.

Targeted Problem

Visualization of the distributions of the input data with effective filters, development of new data structures for handling high dimensional and heterogeneous data and networks, and investigation of how to present and analyse the results with respect to the input data.

Approach

• Our approach is to supply a holistic view of a machine learning system and provide interactive visualization that facilitates human involvement and various stages of machine learning.
• We start with deep learning and computer vision tasks as the initial application domain in the area of facial reconstruction.
• We combine new and traditional data visualization methods and will develop a project demonstrator.

PIs

Anders Ynnerman (LiU), Jianmin Zheng (NTU)