Janick Edinger

Alhassan Abdelhalim, Janick Edinger, Sören Laue et al.

Large Language Models (LLMs) are becoming increasingly popular in pervasive computing due to their versatility and strong performance. However, despite their ubiquitous use, the exact mechanisms underlying their outstanding performance remain unclear. Different methods for LLM explainability exist, and many are, as a method, not fully understood themselves. We started with the question of how linguistic abstraction emerges in LLMs, aiming to detect it across different LLM modules (attention heads and input embeddings). For this, we used methods well-established in the literature: (1) probing for token-level relational structures, and (2) feature-mapping using embeddings as carriers of human-interpretable properties. Both attempts failed for different methodological reasons: Attention-based explanations collapsed once we tested the core assumption that later-layer representations still correspond to tokens. Property-inference methods applied to embeddings also failed because their high predictive scores were driven by methodological artifacts and dataset structure rather than meaningful semantic knowledge. These failures matter because both techniques are widely treated as evidence for what LLMs supposedly understand, yet our results show such conclusions are unwarranted. These limitations are particularly relevant in pervasive and distributed computing settings where LLMs are deployed as system components and interpretability methods are relied upon for debugging, compression, and explaining models.

Emre Gülsoylu, Alhassan Abdelhalim, Derya Kara Boztas et al.

Background: The standardisation of Intermodal Loading Units (ILUs), including containers, semi-trailers, and swap bodies, has transformed global trade, yet efficient and robust identification remains an operational bottleneck in ports and terminals. Objective: To map Computer Vision (CV) methods for ILU identification, clarify terminology, summarise the evolution of proposed approaches, and highlight research gaps, future directions and their potential effects on terminal operations. Methods: Following PRISMA-ScR, we searched Google Scholar and dblp for English-language studies with quantitative results. After dual reviewer screening, the studies were charted across methods, datasets, and evaluation metrics. Results: 63 empirical studies on CV-based solutions for the ILU identification task, published between 1990 and 2025 were reviewed. Methodological evolution of ILU identification solutions, datasets, evaluation of the proposed methods and future research directions are summarised. A shift from static (e.g. OCR-gates) to vehicle mounted camera setups, which enables precise monitoring is observed. The reported results for end-to-end accuracy range from 5% to 96%. Conclusions: We propose standardised terminology, advocate for open-access datasets, codebases and model weights to enable fair evaluation and define future work directions. The shift from static to dynamic camera settings introduces new challenges that have transformative potential for transportation and logistics. However, the lack of public benchmark datasets, open-access code, and standardised terminology hinders the advancements in this field. As for the future work, we suggest addressing the new challenges emerged from vehicle mounted cameras, exploring synthetic data generation, refining the multi-stage methods into unified end-to-end models to reduce complexity, and focusing on contextless text recognition.

Emre Gülsoylu, André Kelm, Lennart Bengtson et al.

Identifying transportation units (TUs) is essential for improving the efficiency of port logistics. However, progress in this field has been hindered by the lack of publicly available benchmark datasets that capture the diversity and dynamics of real-world port environments. To address this gap, we present the TRUDI dataset-a comprehensive collection comprising 35,034 annotated instances across five categories: container, tank container, trailer, ID text, and logo. The images were captured at operational ports using both ground-based and aerial cameras, under a wide variety of lighting and weather conditions. For the identification of TUs-which involves reading the 11-digit alphanumeric ID typically painted on each unit-we introduce TITUS, a dedicated pipeline that operates in three stages: (1) segmenting the TU instances, (2) detecting the location of the ID text, and (3) recognising and validating the extracted ID. Unlike alternative systems, which often require similar scenes, specific camera angles or gate setups, our evaluation demonstrates that TITUS reliably identifies TUs from a range of camera perspectives and in varying lighting and weather conditions. By making the TRUDI dataset publicly available, we provide a robust benchmark that enables the development and comparison of new approaches. This contribution supports digital transformation efforts in multipurpose ports and helps to increase the efficiency of entire logistics chains.

Christian Krupitzer, Tim Wagenhals, Marwin Züfle et al.

Production issues at Volkswagen in 2016 lead to dramatic losses in sales of up to 400 million Euros per week. This example shows the huge financial impact of a working production facility for companies. Especially in the data-driven domains of Industry 4.0 and Industrial IoT with intelligent, connected machines, a conventional, static maintenance schedule seems to be old-fashioned. In this paper, we present a survey on the current state of the art in predictive maintenance for Industry 4.0. Based on a structured literate survey, we present a classification of predictive maintenance in the context of Industry 4.0 and discuss recent developments in this area.

Christian Krupitzer, Sebastian Müller, Veronika Lesch et al.

Industry 4.0 or Industrial IoT both describe new paradigms for seamless interaction between humans and machines. Both concepts rely on intelligent, inter-connected cyber-physical production systems that are able to control the process flow of industrial production. As those machines take many decisions autonomously and further interact with production and manufacturing planning systems, the integration of human users requires new paradigms. In this paper, we provide an analysis of the current state-of-the-art in human-machine interaction in the Industry 4.0 domain.We focus on new paradigms that integrate the application of augmented and virtual reality technology. Based on our analysis, we further provide a discussion of research challenges.