Digital Library of Gesellschaft für Informatik e.V.
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Towards an Augmented Reality System for Obstacle Avoidance in Agricultural Machinery
No full textWe present a work-in-progress augmented reality system designed to support obstacle avoidance in agricultural vehicles. Static hazards such as wells, rocks, or ditches are often obscured by tall crops and lack consistent physical markers, posing a risk to large machinery during field operations. Our prototype uses a handheld tablet and RTK-GNSS to display virtual warning markers that mimic real-world signage. These markers are color-coded and icon-based, conveying obstacle type and urgency with minimal cognitive load. The system segments the field into priority zones based on vehicle dimensions and movement direction. A preliminary field test shows that the augmented reality interface improves hazard awareness even for fully occluded objects. Future work will address user studies, dynamic obstacles, and integration into vehicle-mounted displays
Mind the Gap – Wertgenerierungslücken entlang der Open-Data-Wertschöpfungskette
No full textTrotz des großen Potenzials offener Daten (Open Data) für Innovation und gesellschaftliche Teilhabe bleibt deren Nutzung durch Bürger, Entwickler und Unternehmen hinter den Erwartungen zurück. Hauptgründe hierfür sind unter anderem unzureichende Datenqualität, mangelnde Zugänglichkeit und fehlende Kompetenzen, die zu unüberwindbaren Barrieren während der Wertschöpfung führen können. Der vorliegende Beitrag untersucht die Open-Data-Wertschöpfungskette und identifiziert zentrale Herausforderungen in Form von Wertgenerierungslücken, die die effektive Nutzung von Open Data durch zentrale Stakeholdergruppen (Bürger, Entwickler und Unternehmen) beeinträchtigen. Auf dieser Basis werden spezifische Softwareartefakte vorgeschlagen (Open-Data-App-Portal, KI-Schnittstelle, Rückkopplungskanäle), die den betrachteten Stakeholdergruppen bei der Überwindung der identifizierten Wertgenerierungslücken Unterstützung bieten können. Darüber hinaus wird die besondere Rolle der öffentlichen Verwaltung als Bereitsteller von Open Data und damit als wichtiger Akteur beim Abbau von Nutzungsbarrieren diskutiert. Die vorgeschlagenen Lösungsansätze zielen darauf ab, die Sichtbarkeit und Nutzung von Open Data zu erhöhen und die Kommunikation zwischen Datenbereitstellern und -nutzern zu verbessern, um so einen nachhaltigen und messbaren Mehrwert zu generieren. Despite the significant potential of open data for innovation and societal participation, its utilization by citizens, developers, and businesses falls short of expectations. The main reasons for this include insufficient data quality, limited accessibility, and lacking competencies, all of which can create insurmountable barriers during the value creation process. This paper examines the open data value chain and identifies central challenges in the form of value generation gaps that hinder the effective use of open data by key stakeholder groups (citizens, developers, and businesses). On this basis, it proposes specific software artifacts (open data app portal, AI interface, feedback channels) that can support these stakeholder groups in overcoming the identified value generation gaps. Additionally, the paper discusses the special role of public administration as an open data provider, and thus as an important actor in reducing usage barriers. The proposed solutions aim to increase the visibility and utilization of open data, as well as improve communication between data providers and users, to generate sustainable and measurable added value
Poster: Offset-Value Coding using SIMD Intrinsics
No full textCore operations in database systems are based on sorting, e.g., creating a new b-tree index, merge joins, or grouped aggregations. The required comparisons can be costly due to many or large columns. In order to reuse previous comparison effort, it can be encoded in form of offset-value codes. While hash values can guarantee that two keys are not equal, offset-value codes can also guarantee equality of key and indicate their sort order, making them usable in sorting algorithms. Modern CPUs provide specialized functional units that enable data parallel execution within a single core. In this paper, we report on our initial experiences and measurements for comparisons using SIMD instructions. Our techniques are portable to many architectures based on architecture-agnostic vector types and instructions. Our results demonstrate that hardware-accelerated sorting and merging are available on any CPU with SIMD instructions, i.e., practically any modern CPU
On the dissertation “Scalable SAT Solving and its Application”
No full textThe author’s dissertation, entitled “Scalable SAT Solving and its Application”, advances the efficient resolution of instances of the propositional satisfiability (SAT) problem, one of the prototypical “hard problems” of computer science with many scientific and industrial real-world applications. A particular focus is put on exploiting massively parallel computational environments, such as high-performance computing (HPC) systems or cloud computing. The dissertation has resulted in world-leading solutions for scalable automated reasoning and in a number of awards from the SAT community, and has most recently been acknowledged with a GI Dissertation Award. The article at hand summarizes the topic, approaches, and central results of the dissertation, estimates the work’s long-term impact and its role for future research, and closes with some personal notes
Gender-sensitive urban planning: Connections between gender, perception of safety in public space, and environmental design
No full textThere is a growing trend among cities around the world to integrate sustainability considerations into their strategic agendas. However, the UN Women's report from 2017-2020 points at persistent gaps in achieving the United Nations' 2030 Agenda and its 17 Sustainable Development Goals (SDGs), particularly SDG 5, which aims for gender equality, and SDG 11, which focuses on creating safe, inclusive, and sustainable urban environments. Despite these objectives, the safety and inclusion requirements of women and gender-diverse individuals remain largely unmet. Contemporary urban safety strategies frequently focus on objective risks over subjective safety, thereby failing to acknowledge the substantial influence of perceived safety on individuals' behaviours and their quality of life. This study addresses this research gap by examining the relationship between environmental factors and subjective safety in a medium-sized town in Germany, Bamberg. It investigates the impact of environmental factors on perceptions of safety in the city's public spaces. The methodology will comprise a spatial analysis using virtual mapping of locations that are perceived as unsafe. The findings are intended to inform urban planning practice by identifying gender-specific safety needs and promoting more inclusive and sustainable urban development
FidNET: Flexible Infrastructure for Decentralized Trust Establishment in Industrial Supply Chain Networks
No full textAs industrial supply chains become increasingly complex, establishing digital trust and transparency is crucial for efficient operations. This paper introduces FidNET, a flexible infrastructure leveraging blockchain technology to enhance trustworthiness and transparency within supply chain networks. By utilizing blockchain's immutable and decentralized ledger, FidNET provides a secure platform for verifying transactions and ensuring the integrity of data shared among stakeholders. A concept and architecture are discussed, showing how blockchain technology can be effectively implemented to build digital trust and improve transparency in industrial supply chains
AI-based character generation for disease stories: a case study using epidemiological data to highlight preventable risk factors
No full textData-driven storytelling has grown significantly, becoming prevalent in various fields, including healthcare. In medical narratives, characters are crucial for engaging audiences, making complex medical information accessible, and potentially influencing positive behavioral and lifestyle changes. However, designing characters that are both educational and relatable to effectively engage audiences is challenging. We propose a GenAI-assisted pipeline for character design in data-driven medical stories, utilizing Stable Diffusion, a deep learning text-to-image model, to transform data into visual character representations. This approach reduces the time and artistic skills required to create characters that reflect the underlying data. As a proof-of-concept, we generated and evaluated two characters in a crowd-sourced case study, assessing their authenticity to the underlying data and consistency over time. In a qualitative evaluation with four experts with knowledge in design and health communication, the characters were discussed regarding their quality and refinement opportunities. The characters effectively conveyed various aspects of the data, such as emotions, age, and body weight. However, generating multiple consistent images of the same character proved to be a significant challenge. This underscores a key issue in using generative AI for character creation: the limited control designers have over the output
The role of EBSI in eIDAS - how qualified ledger with governmental trust anchor could shape eIDAS ecosystem
No full texteIDAS 2.0 as a legal and technical framework for trustworthy, decentralized identities in conjunction with the EU digital wallet and various trust services introduced Electronic Ledger as own (qualified) trust service. This could lead to a rise in distributed ledger technologies (DLT) in general and European Blockchain Services and Infrastructure (EBSI) in particular. As EBSI is provided by Member States it contains governmental trust anchor per default and so strength the European digital sovereignty if EBSI could be integrated into eIDAS ecosyste
Towards Complex Table Question Answering Over Tabular Data Lakes
No full textNatural Language Interfaces for Databases (NLIDBs) are an interesting alternative to SQL since they empower non-experts to query data. However, NLIDB approaches require this data to first be integrated into a database schema, which causes high upfront data engineering and integration overheads. As such, Open Table Question Answering (OTQA) is promising, since it allows directly querying the data in data lakes without needing to incorporate it into a schema. Many recent OTQA approaches combine Retrieval-Augmented Generation (RAG) with Large Language Models (LLMs), where relevant tables are first retrieved from a data lake and then used as input to an LLM to answer the user query. In this paper, we take the first systematic step for investigating how LLMs paired with table retrievers can answer queries over private tabular data lakes. As a main finding, we see that even when tuning several parameters of this approach, current LLMs still fail to answer queries that focus on the simple extraction of individual cell values, let alone aggregate queries. Thus, they are far from the rich querying capabilities that NLIDB approaches offer today. To solve this, we recommend promising future work to enable complex question answering over tabular data lakes