DISA

When? May 29, 14.00-15.00
Where? Onsite: D2272 and via zoom
Registration: Please sign up for the seminar via this link https://forms.gle/VXDXziHks8ZYC7kz5
by May 27. This is especially important if you plan to attend onsite so we can make sure there is fika for everyone.

Agenda
14.00-14.10 Welcome and practical information
14.10-14.55 FAVE: A Visual Analytics System for Urban Accessibility Fairness – Parisa Salmanian
14.55 – 15.05 Coffee break
15.05 – 15.50 Security and Digital Forensics for Industrial IoT and Connected Systems – Mohamed Eldefrawy
15.50 -16.00 Sum up and plan for our upcoming seminars

Abstract

FAVE: A Visual Analytics System for Urban Accessibility Fairness – Parisa Salmanian
Who has fair access to essential daily services such as schools, hospitals, and grocery stores, and who does not? In rapidly growing cities, answering this question requires more than aggregate statistics; it requires methods that reveal spatial inequalities across multiple urban scales. This talk presents FAVE (Fairness Accessibility Visual Explorer), an interactive visual analytics system for analyzing urban service accessibility fairness in Swedish cities. FAVE combines gravity-based and distance-based accessibility models with inequality measures, including the Gini coefficient and generalized entropy, in a coordinated interface that also integrates dimensionality reduction, parallel coordinates, and explainable boosting machines. 

The system supports the exploration of accessibility patterns at the district, neighborhood, and building levels, and enables exploratory what-if analysis through urban scenario editing, including additions with the ability to adjust attributes such as footprint, floor area, number of floors, floor height, and shape, modification of POIs and buildings, and changes in building type. It also incorporates LLM-based explanations and intervention suggestions. We demonstrate FAVE using building and OpenStreetMap data together with official statistics from Statistics Sweden (SCB) for Växjö and other Swedish municipalities. The results show how the system reveals spatial inequalities in access to healthcare, education, and other essential services, supporting more informed and evidence-based urban planning. 

Security and Digital Forensics for Industrial IoT and Connected Systems – Mohamed Eldefrawy
Mohamed Eldefrawy will present his current research activities with a focus on Industrial Internet of Things (IIoT) security, digital forensics, lightweight authentication, and secure connected systems. He will also introduce the KK-stiftelsen-funded CyberGuard Academy project, which aims to strengthen practical competence in cybersecurity and digital forensics in collaboration with Swedish industry. The talk will highlight selected past and ongoing work on formal security analysis, lightweight authentication protocols, Internet of Vehicles security, and digital forensics. It will also outline possible areas for future collaboration between Halmstad University and Linnaeus University.

Short bio:
Mohamed Eldefrawy is a Senior Lecturer in Cybersecurity at the School of Information Technology, Halmstad University. His research focuses on IIoT security, lightweight authentication, formal security analysis, and digital forensics. He is currently PI of the KK-stiftelsen-funded CyberGuard Academy project and has published more than 30 peer-reviewed papers. He has served as Guest Editor for IEEE Transactions on Industrial Informatics and Big Data and Cognitive Computing and holds two US patents.

When? May 22, 14.00-16.00
Where? Onsite: D2272 and via zoom
Registration: Please sign up for the seminar via this link, https://forms.gle/6NV1b1tjJ2zUndcn8 by May 20. This is especially important if you plan to attend onsite so we can make sure there is fika for everyone.

Agenda
14.00-14.10 Welcome and practical information
14.10-14.55 Multiscale Mathematics for Complex Systems: From Individuals to Digital Twins – Wolfgang Bock 
14.55 – 15.05 Coffee break
15.05 – 15.50 Multimodal Assessment of Cognitive Load in Technology-Enhanced Learning-Julia Bend
15.50 -16.00 Sum up and plan for our upcoming seminars

Abstracts

Multiscale Mathematics for Complex Systems: From Individuals to Digital Twins – Wolfgang Bock 
Complex systems are often governed by mechanisms acting simultaneously on several spatial, temporal, and organizational scales. In such settings, neither a purely microscopic description nor a purely aggregate model is sufficient on its own. The key difficulty is to relate local interactions, heterogeneity, and stochasticity to robust macroscopic behavior. 

In this talk, I will discuss the mathematics behind multiscale models for complex systems, focusing on the transition from microscopic agent-based or synthetic population descriptions to effective system-level dynamics. This perspective is particularly relevant in applications where one aims to combine structural modeling, uncertainty, and data, for example in the development of digital twins. Synthetic populations serve here as a useful modeling framework in which individual variability and interaction patterns can be represented explicitly, while also raising mathematical questions about emergence, approximation, and the identification of relevant scales. 

The talk is intended as a conceptual introduction to this multiscale viewpoint. Rather than emphasizing technical formalism, I will focus on the key mathematical principles that help make such complex models understandable, interpretable, and computationally tractable. 

Multimodal Assessment of Cognitive Load in Technology-Enhanced Learning-Julia Bend
Technology-enhanced learning (TEL) systems increasingly incorporate adaptive features aimed at improving learning outcomes and supporting individual learners’ needs. However, adaptive learning requires reliable methods for assessing learners’ cognitive states during interaction with digital learning environments.

This presentation focuses on multimodal assessment of cognitive load using eye tracking, EEG, and functional near-infrared spectroscopy (fNIRS). Drawing on cognitive load theory and load theory of selective attention, the work examines how perceptual load and working memory load influence visual attention and cognitive processing during learning tasks. Eye-tracking metrics, such as fixation duration, fixation rate, and blink-related measures, provide behavioral indicators of attentional allocation and processing effort, while EEG and fNIRS offer complementary insight into neural dynamics and cortical activation associated with attention, working memory, and cognitive load.

The presentation discusses current and ongoing work related to distinguishing different types of cognitive load, investigating individual differences in cognitive processing, and exploring how multimodal measures can contribute to adaptive learning systems. Particular attention is given to the potential of combining behavioral and neural measures to support the development of more personalized and cognitively efficient educational technologies.

When? April 10, 14.00-16.00
Where? Onsite: D2272 and via zoom
Registration: Please sign up for the seminar via this link https://forms.gle/gnEjrVfnZRtJgjKYA  by April 8. This is especially important if you plan to attend onsite so we can make sure there is fika for everyone.

Agenda
14.00-14.10 Welcome and practical information
14.10-14.55 Architecting Carbon-Aware Software-as-a-Service – Samuele Giussani
14.55 – 15.05 Coffee break
15.05 – 15.50 Digital product passports: A value perspective – Timmy Öberg
15.50 -16.00 Sum up and plan for our upcoming seminars

Abstracts

Architecting Carbon-Aware Software-as-a-Service – Samuele Giussani
Software-as-a-Service (SaaS) solutions are increasingly adopted due to their scalability, cost-effectiveness, and ability to support rapid deployment while ensuring high availability and flexibility. However, carbon emissions are still rarely treated as a first-class design concern. Existing approaches predominantly focus on post-deployment measurement and mitigation, overlooking the fact that architectural decisions determine a significant share of a system’s environmental impact. This is particularly evident in multi-region and multi-tenant settings, where carbon intensity varies across locations and over time, and tenants contribute unevenly to overall resource consumption. Supporting architects in evaluating these factors early in the design process requires systematic reasoning mechanisms that can combine heterogeneous architectural, infrastructural, and operational information into actionable insights.

In this talk, the conceptual foundations of a Carbon-Aware Reasoning Framework are presented, enabling the design-time evaluation of multi-tenant SaaS architectures with respect to both carbon impact and performance-related quality attributes. A model-driven, tool-supported implementation of the Framework is then introduced, illustrating how carbon-aware reasoning can complement traditional architectural evaluation practices and inform environmentally conscious deployment decisions.

Digital product passports: A value perspective – Timmy Öberg
This presentation provides insights into an ongoing systematic mapping review of digital product passports (DPPs) from a value and ecosystem perspective. Based on existing and emerging classification schemes, the presentation maps the DPP research landscape and discusses current trends. The study explores where current DPP research is focused, including publication venues, research methods, contribution types, and industry sectors. It further analyzes the value dimensions the literature address, including value outcomes, value creation capabilities, and the value chain actors considered.

When? March 20, 14.00-16.00
Where? Onsite:D2272 and via zoom
Registration: Please sign up for the seminar via this link https://forms.gle/qicTg53xmxQJtkBa7 by March 18. This is especially important if you plan to attend onsite so we can make sure there is fika for everyone.

Agenda
14.00-14.10 Welcome and practical information
14.10-14.55 Transparent Adverse Drug Event Detection in Swedish Clinical Text: Challenges in Building Information Extraction Pipelines for a Low-Resource Language – Elizaveta Kopacheva 
14.55 – 15.05 Coffee break
15.05 – 15.50 TWIN4DEM: Strengthening democratic resilience through digital twins – Giangiacomo Bravo 
15.50 -16.00 Sum up and plan for our upcoming seminars

Abstracts

Transparent Adverse Drug Event Detection in Swedish Clinical Text: Challenges in Building Information Extraction Pipelines for a Low-Resource Language – Elizaveta Kopacheva  
Automatic detection of adverse drug events (ADEs) in clinical texts is an important task for pharmacovigilance and patient safety. For clinical decision support, transparency is essential: a useful system should not only classify whether a document mentions an ADE but also highlight the supporting evidence in the text. Achieving this typically requires a pipeline combining named entity recognition (NER), relation extraction (RE), and document classification. However, most prior work studies these components in isolation and primarily focuses on high-resource languages such as English and Chinese. 

This presentation discusses the challenges of developing an end-to-end ADE detection pipeline for Swedish clinical text, a low-resource language. I will discuss how errors propagate through the NER–RE pipeline and affect overall performance. Particular attention is given to the complexity of Swedish clinical NER, including discontinuous entities, partial-word spans, and tokenization mismatches—especially when models rely on English-based tokenizers. I will present a comparison of multilingual and Swedish-specific pretrained models (including a clinically tuned model), as well as encoder-only and encoder–decoder architectures, and discuss their usability for transparent ADE detection in free text. I will also highlight remaining challenges in evaluation. The talk aims to provide practical insights into why building reliable and interpretable ADE detection systems for Swedish clinical text remains difficult and what considerations are important for future work. 

 
TWIN4DEM: Strengthening democratic resilience through digital twins – Giangiacomo Bravo 
Democracy research struggles to explain why democracies backslide and predict which countries are more vulnerable to erosion of the rule of law. TWIN4DEM, a Horizon Europe project, aims to address this issue by creating a digital twin (DT) of political systems. 

DTs are data-intensive simulation models designed as virtual copies of real-world systems. TWIN4DEM focuses on detecting vulnerabilities in democratic systems due to executive aggrandizement and advising policymakers on preventive measures. The current version of the DT is conceptual and focuses on the decision-making process of agents. It is based on synthetic data and represents a “generic” model to be used ad base to implement country-specific ones. The model includes three core groups of agents: (a) members of government that initiate executive aggrandizement; (b) members of parliament; and (c) members of constitutional or administrative courts. Such agents interact with two main types of influencers who shape their behavior: citizens (including context-specific interest groups) and EU institutions. 

The final goal of the project goal is to implement four specific country cases — Czech Republic, France, Hungary, and The Netherlands — reflecting the specificity of the different political systems and informed by local data. The first of these cases (Hungary) is planned to be developed during the spring and some preliminary results will be shared during the seminar. 

When? March 13, 14.00-16.00
Where? Onsite: D2272 and via zoom
Registration: Please sign up for the seminar via this link https://forms.gle/EbRUASvY9c73kMNz6 by March 11. This is especially important if you plan to attend onsite so we can make sure there is fika for everyone

Agenda
14.00-14.10 Welcome and practical information
14.10-14.55 Palme archives as a chatbot – Tibo Bruneel
14.55 – 15.05 Coffee break
15.05 – 15.50 Gradient Tree Boosting for Regression Transfer – Dag Björnberg
15.50 -16.00 Sum up and plan for our upcoming seminars

Abstracts

Palme archives as a chatbot – Tibo Bruneel  
For nearly four decades, the assassination of Swedish Prime Minister Olof Palme has remained a complex and heavily disputed case. Although the investigation officially closed in 2020, the search for answers continues within a monumental digital footprint. This dataset is a chaotic, unstructured web of typed police reports, handwritten notes, maps, and images. 

How can we potentially find previously unknown clues buried within decades of scattered data? 
This presentation introduces “PalmeNet-Chat,” an LLM-powered investigative tool developed by Softwerk AB in collaboration with the true-crime podcast Spår. We will detail the technical challenge of processing this dense archive. By engineering an on-premise OCR pipeline utilising Vision Language Models, we transformed raw history into a structured, searchable library. We will then explore how we implemented Retrieval Augmented Generation (RAG) and vector databases to build a system capable of semantic search and contextual reasoning across the entire case file. Finally, we will offer a glimpse into the next phase of our project, showcasing how we are taking this investigative tool to an entirely new level. 

Gradient Tree Boosting for Regression Transfer – Dag Björnberg  
Many real-world modeling problems are hindered by limited data availability. In such cases, transfer learning leverages related source domains to improve predictions in a target domain of interest. We extend the classical gradient tree boosting paradigm to a regression transfer algorithm by modeling the weak learner as a sum of two regression trees. The trees are fitted on source data and target data, respectively, and jointly optimized for the target data. We derive optimal coefficients for the model update under the least-squares, the least-absolute deviation, and the Huber loss functions. We benchmark our approach against the widely used XGBoost algorithm in several transfer scenarios, achieving superior performance in seven out of eight cases. 

Where? Onsite: D2272 and via zoom
Registration: Please sign up for the seminar via this link https://forms.gle/QB5oiSWMpVofjBHY6 by February 11. This is especially important if you plan to attend onsite so we can make sure there is fika for everyone.

Abstracts
Novelty Detection Using Time-Series Transient Data from the Lead-Acid Forklift Batteries – Zijie Feng  
In industrial applications, monitoring the battery health of electrically powered machinery and detecting abnormal operating conditions is a persistent yet critical challenge. Traditionally, anomaly detection systems are developed reactively: abnormalities are identified only after they occur, often leading to operational disruptions and economic losses. Novelty detection offers an alternative by learning normal behavior and detecting previously unseen abnormalities.  

In this work, we compare a diverse set of data-driven novelty detection methods using simulated time-series transient data derived from real lead-acid forklift battery measurements, aiming to identify suitable solutions for different types of anomalies. 

Potential of Graph Neural Networks for Software Architecture Recovery– Rakshanda Jabeen  
Software architecture recovery (SAR) aims to uncover a system’s modular structure directly from source code, supporting comprehension and maintenance when documentation is missing or outdated. In this work, we investigate the potential of graph neural networks (GNNs) and unsupervised learning for SAR by modeling software systems as heterogeneous, multi-relational graphs. Nodes represent software entities (e.g., files or classes) and typed edges capture structural and functional dependencies such as calls, imports, inheritance, and other code-level relations. To complement dependency structures with meaning, we integrate semantic signals from source code identifiers and related textual artifacts via contextual code embeddings (e.g., Word2Vec), yielding representations that capture both what entities do and how they interact. 

We study heterogeneous GNN encoders that aggregate information across relation types, including heterogeneous graph convolution and heterogeneous attention mechanisms, to analyze the trade-off between fixed normalization and adaptive neighbor weighting in software graphs. On top of these encoders, we explore two unsupervised training paradigms: (i) graph autoencoding, where embeddings are learned by reconstructing observed dependency relations, and (ii) contrastive representation learning inspired by Deep Graph Infomax, which maximizes agreement between embeddings from the original graph and perturbed views. The resulting entity embeddings are clustered to recover candidate architectural modules. Preliminary results across multiple open-source systems indicate that combining semantic cues with structural and functional dependencies produces more meaningful module separation than using structure alone, demonstrating that modern graph representation learning is a promising direction for robust, automated SAR beyond heuristic baselines. 

When? Friday January 23, 14-16
Where? Onsite: D2272 at Linnaeus University in Växjö and online
Registration: Please sign up for the PhD-seminar via this link https://forms.gle/y8hxAvYLzJQHGdR48 by January 21 (especially important if you plan on attending onsite so we have fika for everyone)

Abstracts

Digital product passports: A value perspective
– Timmy Öberg
This presentation provides insights into an ongoing systematic mapping review of digital product passports (DPPs) from a value and ecosystem perspective. Based on existing and emerging classification schemes, the presentation maps the DPP research landscape and discusses current trends. The study explores where current DPP research is focused, including publication venues, research methods, contribution types, and industry sectors. It further analyzes the value dimensions the literature address, including value outcomes, value creation capabilities, and the value chain actors considered.

Designing and Evaluating Socio-Ecological Value Scorecards for Sustainable Smart Agriculture
– Saeed Niksaz
Sustainable farming increasingly uses digital tools such as sensors and artificial intelligence; however, many current evaluation methods do not fully consider the social and governance aspects of agriculture. This seminar brings together results from a systematic literature review and a conceptual model to explore how socio-ecological value scorecards can support sustainable smart farming.

The socio-ecological value scorecard is introduced as an easy-to-use dashboard that combines environmental, social, and economic measures to help farmers and other stakeholders make better decisions in farming systems. The seminar shows the value of scorecards in connecting digital innovation with sustainability goals.

When? Friday December 5, 14-16
Where? Onsite: D0073 and via zoom

Agenda
14.00-14.10 Welcome and practical information
14.10-14.55 Presentation and discussion: 
A Comparative Evaluation of AI-Generated and Human-Written Alt Text for Image Accessibility – Mexhid Ferati
14.55 – 15.05 Coffee break
15.05 – 15.50 Presentation and discussion: 
Leveraging AI to predict review helpfulness and automate assessment in higher education – Zenun Kastrati

15.50 -16.00 Sum up and plan for our upcoming seminars

Abstracts

A Comparative Evaluation of AI-Generated and Human-Written Alt Text for Image Accessibility – Mexhid Ferati
This study investigates the comparative quality of AI-generated and human-written alternative text (alt text) for images, with the goal of understanding their respective strengths, limitations, and potential for supporting digital accessibility. The study was motivated by the growing use of generative AI tools, such as ChatGPT, in content creation, and the need to evaluate their effectiveness in producing accessible image descriptions.

The study follows an experimental design assessing 15 images across five thematic categories: People, Animals, Scenery, Food, Objects. The assessment is conducted for appropriateness in five evaluation criteria: accuracy, conciseness, fluency, comprehensibility, and relevance. The compared text for each image includes an alt text manually written by a professional content creator and generated by ChatGPT using standardized prompts. A pilot survey with eleven participants tested the clarity and functionality of the evaluation process before a final survey collected data from 101 participants in Sweden, who rated pairs of AI-generated and human-written descriptions across the five evaluation parameters.

Leveraging AI to predict review helpfulness and automate assessment in higher education – Zenun Kastrati
This presentation introduces two AI-driven approaches aimed at improving teaching and assessment in higher education. The first approach focuses on predicting the helpfulness of student reviews in online course using a deep learning framework that combines textual features with course metadata and student satisfaction. The second explores the application of large language models (LLMs) for automated scoring and feedback generation. Together, these approaches demonstrate how AI can enhance feedback loops and assessment scalability, while also addressing challenges related to interpretability, rubric clarity, and task subjectivity.