URAI 2024

Session

Main Conference

Nov 14, 2024, 9:00 AM

Contribution list Timetable

26. Keynote 1: Generative AI for Human-Robot Dialogue – Problem Solved?

Dr Maike Paetzel-Prüsmann (Disney Research)

11/14/24, 9:15 AM

Abstract:
“Okay Google, set a timer for 10 minutes” The ability to interact with AI assistants using natural speech has become a reality for millions of households worldwide. Yet, beyond smart speakers, spoken conversations are sparse in interactive devices, despite being one of the most natural forms of human engagement. Even in the field of Human-Robot Interaction (HRI), spoken dialogue is...

9. AI-Guided Noise Reduction for Urban Geothermal Drilling

11/14/24, 10:30 AM

Main Track

Talk

Extended Abstract in appendix...

4. Artificial Intelligence for Quality Assurance and Troubleshooting in Industry

11/14/24, 10:50 AM

Main Track

Talk

Abstract

This paper explores the application of Artificial Intelligence (AI) to contribute to the improvement of quality assurance and troubleshooting in manufacturing. The goal is to identify and resolve quality issues effectively using AI techniques, applying Explainable AI (XAI) to ensure transparency and comprehensibility. We propose three approaches to tackle these industry...

13. A Commissioning-Oriented Fault Detection Framework for Building Heating Systems Using SARIMAX Models

11/14/24, 11:10 AM

Main Track

Talk

A scalable and rapidly deployable fault detection framework for building heating systems is presented. Unlike existing data-intensive machine learning approaches, a SARIMAX-based concept was implemented to address challenges with limited data availability after commissioning of the plant. The effectiveness of this framework is demonstrated on real-world data from multiple solar thermal...

10. Prediction of PV Power Production with Neural ODEs on the base of Weather data

11/14/24, 11:30 AM

Main Track

Talk

full abstract in the appendix
...
Predicting energy production from photovoltaics (PV) is crucial for efficient energy management. In order to be able to apply different operating strategies, it is necessary to be able to predict the expected amounts of PV energy. The operating strategies are typically optimized with regard to economic or technical goals or a combination of both. Within this...

19. Can Machine Learning and Explainable Artificial Intelligence Help to Improve an Expert Model for Predicting Thermomechanical Fatigue?

11/14/24, 11:50 AM

Main Track

Talk

Machine learning (ML) models are increasingly used for predictive tasks, yet traditional data-based models relying on expert knowledge remain prevalent. This paper examines the enhancement of an expert model for thermomechanical fatigue (TMF) life prediction of turbine components using ML. Using explainable artificial intelligence (XAI) methods such as Permutation Feature Importance (PFI) and...

20. AI Adaption within SMEs: Analysis of Impedances and Suggested Approaches

11/14/24, 1:00 PM

Main Track

Talk

This paper examines the most effective means of supporting small and medium-sized enterprises (SMEs) for the integration of artificial intelligence (AI) into their business practises. In addition to the challenges and limitations described in the existing literature, the experiences of the KI-Labor Südbaden will be used as a case study. Although AI has shown rapid technical progress in the...

24. Enhancing Phishing Email Detection with Context-Augmented Open-Source Large Language Models

11/14/24, 1:20 PM

Main Track

Talk

Abstract
Large Language Models offer a promising approach to improving phishing detection through advanced natural language processing. This paper evaluates the effectiveness of context-augmented open-source LLMs in identifying phishing emails. An approach was developed that combines the methods of Few-Shot Learning and Retrieval-Augmented Generation (RAG) to significantly improve the...

14. Content Creation with GenAI: Use Case on ClimaClic´s Instagram Marketing

11/14/24, 1:40 PM

Main Track

Talk

Abstract. The availability of generative artificial intelligence (GenAI) tools has substantially increased, resulting in numerous positive impacts on the marketing sector. However, issues related to misinformation and deepfakes, biases and fairness, privacy, and ethical concerns, among others, have been highlighted. This research aims to examine the effects of utilizing GenAI for text, image,...

15. AI Chatbots in Design Thinking

11/14/24, 2:00 PM

Main Track

Poster

In recent years, the integration of artificial intelligence (AI) into various business processes has attracted significant attention. This paper explores the use of generative AI, specifically Large Language Models (LLMs) such as ChatGPT, to improve the Design Thinking (DT) process in business and IT consulting. The primary focus of this paper is on the technical implementation in practice and...

3. Can Biases in ImageNet Models Explain Generalization?

11/14/24, 2:00 PM

NECTAR

Nectar Poster

The robust generalization of models to rare, in-distribution (ID) samples drawn from the long tail of the training distribution and to out-of-training-distribution (OOD) samples is one of the major challenges of current deep learning methods. For image classification, this manifests in the existence of adversarial attacks, the performance drops on distorted images, and a lack of generalization...

8. Data augmentation for pathogen segmentation in vinewood fluorescence microscopy images

11/14/24, 2:00 PM

Main Track

Poster

In this paper, we address the problem of segmentation of pathogens within fluorescence microscopy images. To our knowledge, the quantification from such images is an original problem.
As a consequence, there is no available database to rely upon in order to use supervised machine learning techniques. In this paper, we provide a workaround by creating realistic images containing the desired...

23. Evaluating AI-Generated Solution Ideas: A Comparative Study of AI and Human Assessments for Sustainable Process Design

11/14/24, 2:00 PM

Main Track

Poster

The integration of generative artificial intelligence (AI) in sustainable process design has gained substantial traction, with AI increasingly employed to generate innovative solution ideas. However, the efficacy of these AI-generated ideas requires rigorous evaluation to ensure their usefulness, feasibility, novelty, and sustainability. This study examines the reliability of AI evaluations by...

11. Exploration of Neural Network Architectures for Inertia Parameter Identification of a Robotic Arm

11/14/24, 2:00 PM

Main Track

Poster

In this paper, we propose a machine learning based approach for identifying inertia parameters of robotic systems. The method is evaluated in simulation and compared against classical methods. Therefore, parameter identification based upon a numerical optimization is implemented and tested on ground truth data. For a case study, the physical simulation of a four degree of freedom robot arm...

17. Leveraging Generative AI for Enhancing Domain-Driven Software Design

Mr Götz-Henrik Wiegand (Hochschule Karlsruhe - University of Applied Sciences, Germany)

11/14/24, 2:00 PM

Main Track

Poster

Domain-Driven Design (DDD) is a key framework for developing customer-oriented software, focusing on the precise modeling of an application's domain. Traditionally, metamodels that describe these domains are created manually by system designers, forming the basis for iterative software development. This paper explores the partial automation of metamodel generation using generative AI,...

2. Retail-786k: a Large-Scale Dataset for Visual Entity Matching

11/14/24, 2:00 PM

Nectar Poster

Entity Matching (EM) defines the task of learning to group objects by transferring semantic concepts from example groups (=entities) to unseen data. Despite the general availability of image data in the context of many EM-problems, most currently available EM-algorithms solely rely on (textual) meta data.

In this paper, we introduce the first publicly available large-scale dataset for...

7. Semi-supervised mold differentiation using typical laboratory results as label data

11/14/24, 2:00 PM

Main Track

Poster

The paper discusses a project aimed at automating the differentiation of mold samples to ensure clean air in offices and production facilities, using deep neural networks to reduce the time and cost of manual differentiation. Two classification models, EfficientNet V2 and Normalization-Free Net (NFNet), were trained on artificially created data to identify five classes of mold plus an "other"...

21. Specification and Identification of Relationships between Products in the Food Segment

11/14/24, 2:00 PM

Main Track

Poster

Identifying and classifying similar or identical food products in e-commerce and retail is challenging due to diverse data formats, inconsistent product descriptions, and varying detail levels across sources. But precise product matching can optimize databases, enhance customer experience, enable personalized recommendations, and improve competitor price analyses. Current work primarily...

18. Supervised Transfer Learning Framework for Fault Diagnosis in Wind Turbines

11/14/24, 2:00 PM

Main Track

Poster

In Germany, electricity from renewable energy sources is primarily generated from wind energy.
To support the EU's renewable energy targets, it is important to reduce wind turbine downtime and prevent damage. This can be achieved through condition monitoring and intelligent fault diagnosis, which increases the energy yield.

Supervised learning is a method for performing intelligent...

22. The Potential of Generative AI for Systematic Engineering Innovation

11/14/24, 2:00 PM

Main Track

Poster

This paper explores the application of generative AI for systematic innovation and inventive engineering problem solving. Using an automated prompt generation approach, including iterative problem definition and multi-directional promting with numerous elementary solution principles, the study investigates the ability of AI chatbots to autonomously generate solution ideas and create and...

1. Urban Sound Propagation: a Benchmark for 1-Step Generative Modeling of Complex Physical Systems

11/14/24, 2:00 PM

NECTAR

Nectar Poster

Data-driven modeling of complex physical systems is receiving a growing amount of attention in the simulation and machine learning communities. Since most physical simulations are based on compute-intensive, iterative implementations of differential equation systems, a (partial) replacement with learned, 1-step inference models has the potential for significant speedups in a wide range of...

25. Keynote 2: Regulating the Synthetic Society

Prof. Bart van der Sloot (Tilburg University, Netherlands)

11/14/24, 3:00 PM

Abstract: Generative Artificial Intelligence, Large Language Models and Humanoid Robots are increasingly used for educational purposes as well as for the treatment of patients, put to use by the entertainment and creative industries, and applied in the retail and financial sectors. At the same time, these technologies are used for fraud, deception, exploitation, identity-theft and...