Description: Water supply and wastewater treatment plants are steadily transforming from traditional physical infrastructures to
cyber-physical systems (CPS). Digitalization brings new vulnerabilities and attack surfaces for attacks from cyberspace. There has been an increase in reported cyberattacks on water management assets, demonstrating that working preventive measures are as necessary as early detection and location of attacked system components. Traditional mechanisms for detecting cyberattacks are becoming increasingly ineffective. Our contribution to the project is to develop advanced and robust AI-based tools for early attack detection and better situational and risk assessment.

Involved researchers:

Description: Electronics-based systems (EBS) are becoming more and more prevalent in production, infrastructure and transport, but are only accepted if people trust these systems. The researchers in the doctoral programme Dependable ElectroNIc-Based SystEms (DENISE) will explore concepts, methods and application-oriented tools to make EBS more reliable. The project deepens the very good relationship between FH Joanneum and Graz University of Technology through a joint doctoral programme. The aim of our team in the project is to make AI-powered EBS more trustworthy. This is particularly challenging when a model is trained or refined on an embedded device using locally and autonomously acquired data with little or no supervision.

Selected publications:

• REDS: Resource-Efficient Deep Subnetworks for Dynamic Resource Constraints, arXiv 2023

Involved researchers:

Description: The project aims to develop an infrastructure for cognitive (IoT-based and AI-based) vehicle fleet monitoring, which includes collection, evaluation, interpretation and use of vehicle data in the context of various data‐driven services. The project will cover several services and use cases for data‐driven support and expansion of the development processes as well as the provision of services on the vehicle (e.g., predictive maintenance). The fleet data can be utilized to detect new trends in mobility. Our goal is to develop new methods to achieve fast data collection in automotive applications, design and development of novel and efficient algorithms and AI-based data processing methods for automotive environments. (1) We will enable rapid prototyping of onboard measurement for fast data collection and method development, (2) modular device design to capture use‐case specific data and enable future extensions (sensors and services), and (3) capture, interpret and preprocess data on the device for responsive local services.

Involved researchers:

Description: Deep learning methods are increasingly used to solve complex tasks, yet little is known about the choice of the best training data, architecture, and model capacity to match available hardware and energy resources. The trade-offs and optimization potential in this space are not well understood, and are being explored in this project. We also aim to develop deep learning models that show reliable generalization not only for conventional in-distribution but also for out-of-distribution scenarios. We focus on resource constrained IoT devices and their autonomous operation on the edge, where the data is scarce and environmental dynamics is non-stationary. To achieve the goal we study the loss landscape of deep neural networks and try to disentangle the impact of model’s architecture, overparameterization, training data, augmentation, training objectives, hyperparameters, regularization, etc. on generalization.  We also look at optimized models operating in resource-constrained environments from two perspectives: (1) their generalization and robustness and how to improve these, and (2) how to make compressed models run efficiently on specialized hardware.

Selected publications:

• Subspace-Configurable Networks, CoLLAs'24
• REPAIR: REnormalizing Permuted Activations for Interpolation Repair, ICLR'23
• The Role of Permutation Invariance in Linear Mode Connectivity of NNs, ICLR'22
• Understanding the Effect of Sparsity on Neural Networks' Robustness, ICMLw'21

Involved researchers:

Description: Low-cost environmental sensors and environmental models present interesting use-cases for testing, optimizing and improving machine learning models. For example, low-cost gas sensors may drift over time or their measurements may be affected by other environmental processes and environmental dynamics. Sophisticated machine learning models for accurate sensor calibration can help to compensate for these effects. On-device sensor data processing, however, may face severe resource constraints including processing power, memory and energy budget, that need to be taken into account. Another example is that some types of chemical gas sensors are power-hungry and machine learning can be used to replace actual measurements with high-quality predictions. Finally, sensor data coming from IoT sensors measuring gases and particle concentrations in the ambient air help to push the limits of today's air quality maps by extending the conventional networks of static high-quality measurement stations with dense IoT measurements. The challenge is to show that it is possible to build high-quality and high-resolution air quality maps using low-cost, less precise, low-resolution, less stable, noisy sensors. While successfully solving this challenge, we managed to improve the accuracy of air quality models by accounting for air pollution transfer, and used our models to understand the impact of COVID-19 lockdown measures on local air quality.

Recent publications (see full list here):

• Geometric Data Augmentations to Mitigate Distribution Shifts in Pollen Classification from Microscopic Images, IEEE ICPADS'23
• TIP-Air: Tracking Pollution Transfer for Accurate Air Quality Prediction, CPD'21
• Compensating Altered Sensitivity of Duty-Cycled MOX Gas Sensors with ML, SECON'21
• Understanding the Impact of Lockdown Measures on Local Air Quality, UrbCom'21

Involved researchers: