DISA

When? Friday March 21 14-16
Where? Onsite: D2272 and via zoom
Registration: Please sign up for the PhD-seminar via this link https://forms.gle/XmL6bguq3T4Lax71A by March 19th (especially important if you plan on attending onsite so we have fika for everyone)

Agenda

14.00-14.10 Welcome and practical information from Welf Löwe
14.10-14.55 Presentation and discussion: Immersive Analytics for Understanding Ecosystem Services Data – Benjamin Powley
14.55 – 15.05 Coffee break
15.05 – 15.50 Presentation and discussion Bridging Theory and Practice: AI-Driven Insights into Manufacturing Evolution and Industrial Maintenance Innovation – Muntaser Nuttah
15.50 -16.00 Sum up and plan for the April seminar

Abstracts

Immersive Analytics for Understanding Ecosystem Services Data – Benjamin Powley
When planning land use decisions, the input from experts in various domains is often required when making the decision. Ecosystem services analysis is often performed by expert analysts to estimate the effect of land use changes on the environment. For example, farming provides the benefit of agricultural productivity, but can negatively impact ecosystem services by reducing biodiversity, or increasing the amount of nitrogen in waterways.

In this talk, immersive VR visualization system, Immersive ESS Visualizer, is presented. The visualization system was designed for the comparison of multiple ecosystem services across different land use change scenarios. A user study was performed to evaluate the effectiveness of Immersive ESS Visualizer for ecosystem services analysis tasks compared to existing media (paper maps, and PDF’s on a 2D screen). The results of the user study will be discussed.

Bridging Theory and Practice: AI-Driven Insights into Manufacturing Evolution and Industrial Maintenance Innovation – Muntaser Nuttah
“In today’s industrial landscape, artificial intelligence (AI) is critical for transforming data into actionable knowledge. This talk highlights two innovative studies that leverage AI to decode complex unstructured datasets. The first study employs Natural Language Processing (NLP), Large Language Models, and Dynamic Topic Modeling to conduct a large-scale review of over 35,000 publications in manufacturing digitalization and automation from 1970 to 2023. This approach not only structures a fragmented body of knowledge but also tracks thematic evolutions—from early simulation and scheduling studies to emerging trends in energy efficiency, composite materials, cybersecurity, robotics, and AI—offering empirical support to creative destruction and technological paradigm theories. Similarly, the second study transitions to practical application, demonstrating how NLP-driven text mining could be used to deal with unstructured maintenance logs, claims, and work orders from Volvo CE. By converting raw text into structured insights, the framework enables proactive maintenance planning, system optimization, and knowledge transfer—showcasing AI’s capacity to bridge data volume and expert interpretation in industrial settings.”

When? Friday March 7th 14-16
Where? Onsite: D1140 at Linnaeus University in Växjö and online
Registration: Please sign up for the PhD-seminar via this link https://forms.gle/kydkchwh92y9g2RC9 by March 5th (especially important if you plan on attending onsite so we have fika for everyone)

14.00-14.10 Welcome and practical information from Welf Löwe
14.10-14.55 Presentation and discussion: Producing the Next Generation of Forest Attribute Maps – the Swedish Case – Dag Björnberg
14.55 – 15.05 Coffee break
15.05 – 15.50 Presentation and discussion: Sound, Precise, Memory Efficient P2A and beyond – Mathias Hedenborg
15.50 -16.00 Sum up and plan for our seminars in April

Abstracts

Producing the Next Generation of Forest Attribute Maps – the Swedish Case – Dag Björnberg
Remote sensing techniques are widely used for mapping and monitoring forest attributes, providing valuable information on forest cover, biomass, and overall forest health. In recent years, national airborne laser scanning (ALS) campaigns have been conducted in several countries to map forest resources. When combining ALS data with field inventory data, these datasets enable the development of nationwide models for prediction of forest attributes. In this talk, we discuss the potential of machine learning (ML) to enhance existing modeling approaches for nationwide forest attribute mapping in Sweden, and show prediction results on five forest variables.

Sound, Precise, Memory Efficient P2A and beyond – Mathias Hedenborg
Points-to analysis can be used as a helping tool, but then it needs to be sound, fast, and precise.
The Points-to information can be useful in Compiler Optimization and Software Engineering.

In this thesis, an approach is presented that fulfills all of these requirements. The approach is flow-sensitive since it is an SSA-based data-flow analysis.
By using X-terms (chi-terms) for saving context data, the approach will be context-sensitive.

We describe how the soundness is reached, by relate the use of X-terms to a conservative data-flow analysis.
The proof will show that the steps in creating X-term based representation will guarantee the soundness, if the conservative data-analysis is sound.

We will also show that the use of X-terms out-range other traditional representation for the context information needed.

There will also be a discussion about the precision in a system using X-terms.

In addition to this, the thesis discusses how points-to analysis can be used in other areas like program/system understandability and Compiler Optimization.
Future work will point out areas like result prognosis, alias, reachability, security, and more areas related to Software Engineering.