Writing scientific papers is essential for advancing any given research field, yet undergraduate and graduate students often struggle with this task, facing challenges in clearly presenting their ideas and results. Valuable scientific contributions described in papers that are not well-structured and not easy to follow may not get published. This paper addresses these challenges by proposing a framework called STRaWBERRY, which provides a checklist-based guide to help evaluate individual components of paper drafts against essential quality criteria. Additionally, we propose the use of Large Language Models (LLMs) to automate the assessment based on these criteria. The LLM evaluation process encourages active learning (in the educational sense) and allows the drafts to be iteratively refined through feedback from the LLM. We evaluate the STRaWBERRY framework by its use in lectures and the corresponding outcome: STRaWBERRY has been successfully used in 10 university courses, leading to multiple student pub-lications. Furthermore, we evaluate the LLM-based approach by assessing its accuracy in evaluating a selection of sample papers, demonstrating its potential to supplement and enhance traditional proofreading cycles.

The rapid evolution of football (soccer) analytics has been driven by advances in structured data analysis and recently also by Large Language Models (LLMs). However, existing methods often fail to adapt dynamically to evolving queries and lack contextual richness. This paper presents a novel retrieval augmented generation (RAG) approach tailored to descriptive football analytics, which leverages spatio-temporal and opponent-related data to transform structured event and player data into actionable insights. Our approach was evaluated using a subset from the 2023/24 season of the first German division (1. Bundesliga) over multiple game weeks, achieving an average accuracy of 63.3% in generating responses, setting a first benchmark. In particular, our approach demonstrated strong performance in answering temporal and spatial queries with an accuracy of 70%, while challenges in player-specific queries highlight opportunities for further refinement. These results underscore the potential of RAG to improve decision making for analysts, sports journalists, and potentially coaches by providing dynamic and query-specific insights, paving the way for advanced applications in descriptive sports analytics and interdisciplinary approaches in digital transformation.

Phishing attacks continue to be a major cyber security problem, leading to an increasing number of studies looking at defense strategies. Therefore, we propose an LLM-based phishing detection approach that enhances work by Koide et al. by extending the prompts with URLs, adapting the Chain-of-Thought (CoT) and incorporating additional parameters. Our approach calculates a phishing score, which is used for the classification of websites as either phishing or non-phishing. Subsequently, the results of our research should enable the development of more effective LLM-based phishing detection systems and aim to improve cyber security defenses against this threat.

Text-to-image models, which aim to convert text input into images, have gained popularity partly due to their flex-ibility and user-friendliness. However, there are still weaknesses in the generation of images intended to display emotions, visual text, multiple objects, relative positioning, and attribute binding. This study analyzes the weaknesses of three open-source models: Stable Diffusion v2-1, Openjourney, and Dreamlike Photoreal 2.0. The models are compared based on scores for quality, alignment, and aesthetics. The evaluation is based on (a) the metrics ClipS core, Frechet Inception Distance (FID), and Large-scale Artificial Intelligence Open Network (LAION) and (b) human perception obtained in user surveys. The evaluation revealed that all models show predominantly unsatisfactory performance, and the identified weaknesses were confirmed.

The tremendous advances in Artificial Intelligence (AI) open new opportunities for education, with Intelligent Tutoring Systems (ITS) powered by Generative Artificial Intelligence (GenAI) proving to be a promising prospect. Because of this, our work explores state-of-the-art (SOTA) ITS approaches with the integration of Large Language Models (LLMs) to improve programming education. We investigate whether and how a GenAI-based ITS can effectively support students in learning R programming skills. We measured the performance of three current pairings of LLMs and user interfaces: GPT-3.5 via ChatGPT, PaLM 2 via Google Bard, and GPT-4 via Bing. Therefore, we evaluated the LLMs on four types of problem settings when learning/teaching programming. Our experimental results show that the use of generative AI, specifically LLMs for R programming, is promising, where GPT-3.5 yielded the most satisfactory results. Furthermore, the advantages and limitations of our approach are addressed and revealed. Finally, open research directions towards explainable AI (XAI) and integrated self-assessment are pointed out.

The coronavirus disease 2019 (COVID-19) has had and continues to have a major impact on public health worldwide. Therefore, early detection of COVID-19 is of great importance to control the spread of the pandemic. In the course, several Machine Learning (ML) and Deep Learning (DL) approaches using various imaging modalities such as X-ray, CT, or ultrasound images have been introduced to enable faster detection and better decision making. In this context, this paper provides an overview of the state of the art (SOTA) and the development of ML and DL in the detection of COVID-19. Based on a Systematic Literature Review (SLR), a comprehensive and systematic tabular was created with the key aspects of the identified articles, such as the image modality, the techniques used for recognition, the dataset, the number of images, and the performance metrics of the developed approach. In addition, the advantages and disadvantages of the individual approaches are discussed and the need for further research is identified, which is also intended to help fight against potential future diseases.

Audio Classification using Machine Learning (ML) techniques has gained significant importance in various domains such as speech recognition, music Classification, and environmental sound analysis. Especially in combination with Transfer Learning (TL), this is a promising technique, which is why we conduct a Systematic Literature Review (SLR) on approaches in this domain, with a focus on sound Classification for monitoring tasks, which differ significantly from speech and music Classification. Furthermore, we provide an overview of TL techniques and applications, considering different methods due to the inherent characteristics of acoustic sound data. Based on our SLR, the advantages and disadvantages of the approaches are highlighted, and further research needs are identified.

Fan sound Classification is a challenging task due to the complexity of acoustic conditions and the distinctive characteristics of different microphones. This study presents an in-depth analysis of fan sound Classification using Federated Learning (FL) across three microphone setups. We evaluate the impact of microphone variations on the performance of the five FL strategies - FedAvg, FedAdagrad, FedYogi, FedAdam, and FedMedian - and explore the potential of FL for decentralized audio Classification. Our comprehensive comparison of these strategies identifies FedYogi as the most effective, demonstrating exceptional adaptability and robustness across diverse acoustic conditions. This investigation not only sheds light on the complex dynamics between microphone variations and Classification accuracy, but also offers deep insights into the application of FL for sound-based equipment monitoring. Furthermore, our findings underscore the significant impact of microphone selection on the efficacy of FL strategies, reinforcing the need for careful consideration of hardware in the deployment of FL systems.

This study evaluates the effectiveness of transfer learning models in industrial surface defect detection using few-shot learning. Surface defect detection is a critical task in various industrial applications, where accurately detecting and classifying defects can improve product quality and increase manufacturing efficiency. However, data scarcity is a considerable challenge: obtaining and labelling defect samples is a costly, time-consuming process and difficult due to their infrequent occurrence. Few-Shot learning aims to effectively train models using only a limited number of labelled samples, thus mitigating the impact of data scarcity. This study compares the performance of transfer learning models pre-trained on three different data sets for few-shot learning in the context of surface defect detection. On the one hand, transfer learning models pre-trained on the ImageNet data set yield the best overall results in terms of accuracy. On the other hand, our results indicate that the DAGM data set, an industrial optical inspection data set which is close to the target domain, is particularly effective for training models to clearly detect surface defects in a few-shot learning scenario.

As the amount of available data in healthcare has increased significantly and only 20% of electronic health record data are in a structured format, data lakes have become a common solution for managing heterogeneous data in the healthcare domain. Nowadays, these are utilized far below their capabilities in medical research. Since previous reviews only partly address data lakes in the healthcare domain, a systematic literature review on this topic is missing. Therefore, this paper provides an overview of applications in the healthcare domain that benefit from data lakes. We review the literature and structure it according to data sources and players, and we identify applications and future research needs of data lakes in the healthcare domain. Overall, it turned out that all players could benefit from the capabilities of data lakes. We found that data lakes are currently not broadly implemented in the field, and the viewpoint of hospital operators and healthcare insurers seems to be an underresearched topic compared to the other players.

In the era of digital transformation, supply chain management faces major challenges induced by the lack of transparency and the evolving industry. In this context, blockchain technology has emerged as a possible answer to the future problems of the supply chain. In this paper, we present a systematic literature review on blockchain in the context of supply chains. The goal of our work is to present the factors and capabilities of blockchain technology that contribute to improving supply chain resilience. We also show which supply chain management factors limit the use of blockchain technology. Based on this, we identify various areas and applications of blockchain technology to support supply chains and highlight current work in the field. From the reviewed literature, we deduce a number of open challenges regarding the application of blockchains in the context of supply chains, e.g. the need (a) to improve the implementation process, (b) to make blockchain more cost-effective, (c) to educate potential users regarding blockchain security aspects, and (d) to further digitize supply chains as part of the digital transformation process.

In machine learning, the presumably best model is selected from a variety of model candidates generated by testing different model types, hyperparameters, or feature subsets. The advent of deep learning has made model selection even more challenging due to the huge parameter search space. Relying on a single metric to select the best model does not consider class imbalances or the different costs of misclassifications. We argue that incorporating human knowledge to interactively analyse the per-class errors and class confusions over all model candidates enables a more efficient training process and yields better models for given applications. This paper proposes the model-agnostic approach ConfusionVis which allows to comparatively evaluate and select multi-class classifiers based on their confusion matrices. This contributes to making the models’ results understandable, while treating the models as black boxes. Therefore, we propose a novel method to measure and visualise distances between confusion matrices and an interactive query interface to incorporate all composition levels of class errors. The approach is evaluated in a user study and the applicability is shown by a case study where marine biologists investigate the conservation efforts of baleen whales by classifying whale species in acoustic recordings. ConfusionVis is available online: https://www.ml-and-vis.org/confusionvis.