P352 - INFORMATIK 2024 - Lock in or log out? Wie digitale Souveränität gelingt
Auflistung P352 - INFORMATIK 2024 - Lock in or log out? Wie digitale Souveränität gelingt nach Erscheinungsdatum
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- KonferenzbeitragMan vs. machine: A study comparing super-recognizers and artificial intelligence(INFORMATIK 2024, 2024) Lietsch, Maria; Preuß, Svenja; Becker, Sven; Labudde, DirkThis study addresses the limits of human and artificial intelligence (AI) in face recognition using a specially designed test, which consists of tasks regarding person identification and lookalike discrimination. It was divided into nine sets of four or five queries each. The assignments, presented in the study, were performed by ten super-recognizers from the Chemnitz police department (Saxony) as well as the AI systems “Face Recognition” and “GhostFaceNet”. The evaluation revealed considerable differences in the results of the individual super-recognizers (SR). Additionally, the comparison between human and artificial intelligence in particular revealed clear limitations of the AI in relation to the tasks set. To further evaluate the super-recognizers and AI systems, additional tests are planned, covering various topics such as the identification of siblings or the recognition of faces aged by AI.
- KonferenzbeitragTowards Ethical Agency in the Smart Home “Living Place”: On the Conception and Development of Ethical Smart Home Systems by Elective Projects within Computer Science Education(INFORMATIK 2024, 2024) Draheim, Susanne; Sudeikat, JanSmart Home applications exert immediate influence on inhabitants. While the widespread availability of supporting frameworks and technologies facilitates ad hoc application development, assessing and designing the impact on inhabitants have to be considered as well. In this paper, we outline a concept for an elective bachelor's project for computer science students planned for the upcoming winter term. This course builds on our experience with two elective courses on the topic of "machine ethics”. In this project, we understand the smart home "LIVING PLACE" at HAW Hamburg and its interior as ethical actors and outline how to advance this viewpoint to a testbed for experimenting with principles of (machine) ethics and embedding ethical values during system development.
- KonferenzbeitragAutomatisierte prädiktive Analytik in der Gepäckabfertigung(INFORMATIK 2024, 2024) Dohrn, Finn; Tropmann-Frick, MarinaZiel dieser Arbeit ist die Entwicklung und Validierung eines automatisierten Prognosemodells für Gepäckmengen am Hamburger Flughafen unter Verwendung der Low-Code AutoML-Bibliothek PyCaret. Durch die Automatisierung signifikanter Phasen des Machine-Learning-Lebenszyklus konnten präzise Vorhersagen für Gepäckstücke pro Flug innerhalb und außerhalb der Flugsaison erreicht werden. Die Ergebnisse zeigen eine Verbesserung der Vorhersagegenauigkeit um 38,6 % gegenüber herkömmlichen Methoden, was die Effizienz in der Personaldisposition maßgeblich unterstützt. Der Einsatz von AutoML ermöglicht zudem eine zeitökonomische Modellentwicklung durch Endanwender. Der Einsatz und Ausbau des autoDS-Moduls kann den bereits hohen Automatisierungsgrad weiter erhöhen. Zukünftige Arbeiten sollten den Einsatz von assistenzgesteuerter Datenvorverarbeitung mit großen Sprachmodellen und Hyperparameteroptimierung für AutoML-Parameter untersuchen, um die Anwendbarkeit und Genauigkeit weiter zu verbessern.
- KonferenzbeitragNode and Edge Removal on Complex Networks in Labor Market Research and their Influence on Centrality Measures(INFORMATIK 2024, 2024) Mangroliya, Meetkumar Pravinbhai; Dörpinghaus, Jens; Rockenfeller, RobertThis research examines the impact of node and edge removal strategies on centrality measures within complex networks. Investigating random, scale-free, and small-world networks, various removal approaches, including targeted and random removal, are evaluated. The study assesses their influence on centrality metrics such as degree, betweenness, closeness, and eigenvector centrality on random networks and networks from educational research describing longitudinal data in labor market-related topics in social networks. The findings contribute insights applicable across domains. In social network analysis, an understanding of key actors is beneficial for the development of targeted interventions or marketing strategies. Historical network analyses benefit from the discernment of pivotal nodes or connections, which elucidate information flow or influential figures across different periods. Such applications underscore the significance of the research in optimizing network performance in diverse contexts.
- KonferenzbeitragGenerative AI and Gametheory for the development and deployment of Honeypots to enhance the Security of Industrial Automation and Control Systems(INFORMATIK 2024, 2024) Peters, Ludger; Gkoktsis, GeorgiosThe computing hardware and software of modern Industrial automation and control system has evolved to be like traditional IT hardware in the first decade of this century. Due to the specialized demands on these systems introduced, e.g., by specialized measurement equipment or additional safety requirements, typical IT update and security procedures cannot be followed. This paper explores the use of generative AI models in honeypots for enhancing the cybersecurity in industrial automation and control systems. As honeypots are used as traps for system attackers, the deployment of generative AI models enables the creation of more convincing and sophisticated decoy environments. This increases the likelihood of an attacker’s engagement with the environment, improving the detection and analysis of malicious activities. Through a brief summary, this paper quantifies the existing research on generative AI in honeypots. The findings highlight the significant potential of generative AI models in enhancing the security of IACS through their integration into honeypot systems. This can ultimately lead to organizations being able to gain more in-depth insights into emerging cyber threats, improve their incident response capabilities, and enhance the resilience of their industrial control systems. To quantify the impact of employing such advanced deception technologies on the behavior of the attacker, this paper proposes a novel approach using a non-cooperative game-theoretic framework for deploying honeypots in OT systems. This methodology enables strategic analysis that balances limited resources with the need to predict and counter sophisticated cyber adversaries’ actions.
- KonferenzbeitragPublic and Expert Insights into Generative AI: The potential for the Financial Industry(INFORMATIK 2024, 2024) Zacharias, JanIn the last few years, generative artificial intelligence (gen AI) has become a success factor in various sectors, including the financial industry. Understanding how the industry perceives gen AI is vital for its successful integration. Therefore, we conducted a mixed-methods study consisting of sentiment and subjectivity analyses of finance-related Reddit discussions, combined with expert interviews from global financial institutions. Whereas the public sentiment has a cautious optimism, experts express both strong support and concerns about gen AI implementations in financial institutions. This study contributes to the academic and practical understanding of gen AI’s real-world implications, highlighting the need for well-considered implementation strategies in the financial industry.
- KonferenzbeitragAI Defenders: Machine learning driven anomaly detection in critical infrastructures(INFORMATIK 2024, 2024) Nebebe, Betelhem; Kröckel, Pavlina; Yatagha, Romarick; Edeh, Natasha; Waedt, KarlPrevious studies have evaluated the suitability of different machine learning (ML) models for anomaly detection in critical infrastructures, which are pivotal due to the potential consequences of disruptions that can lead to safety risks, operational downtime, and financial losses. Ensuring robust anomaly detection for these systems within a company is vital to mitigate risks and maintain continuous operation. In this paper, we utilize a time-series labeled dataset obtained from a hydraulic model simulator (ELVEES simulator) to conduct a comprehensive and comparative analysis of various ML models. The study aims to demonstrate how different models effectively identify and respond to anomalies, underscoring the potential artificial intelligence (AI) driven systems to mitigate attacks. With the chosen approach, we expect to achieve the best performance in detecting two types of anomalies: point anomaly and contextual anomaly.
- KonferenzbeitragAktuelle Ansätze zum Einsatz von Verfahren der automatisierten Bilderkennung mittels maschinellen Lernens im Bereich des Umweltmonitorings(INFORMATIK 2024, 2024) Galle, ChristopherDie steigende Nachfrage nach präzisen aktuellen Erhebungen des Naturzustands hat die Notwendigkeit neuer Herangehensweisen an die Datenerfassung und -auswertung deutlich gemacht. Die Auswertung von Umweltdaten ist eine zeitaufwändige und ressourcenintensive Aufgabe, die eine erhebliche Beteiligung qualifizierten Personals erfordert. Die Automatisierung dieser oft manuellen Prozesse gestaltete sich über viele Jahre hinweg als herausfordernd. Besonders die Artenbestimmung von Insekten und die Auswertung von Wildkameraaufnahmen im Bereich der Ökologieforschung dienen als Beispiele dafür. In Fangflaschen konservierte Insekten müssen von Fachpersonal identifiziert werden, was aufgrund von Beschädigungen an den Insekten sowie dem Verfall während der Lagerung und Bearbeitung ein zeitaufwendiger und zeitkritischer Prozess ist. Aber nicht nur die Auswertung herkömmlicher Bilder und Proben ist für Anwendungen der automatisierten Bilderkennung interessant, auch nicht-fotografische Bilddaten wie Sonar-, Satelliten- oder spektroskopische Aufnahmen eignen sich dafür. Die Verwendung von Methoden des maschinellen Lernens, insbesondere der Einsatz von Convolutional Neural Networks, hat sich hier in vielen Bereichen als äußerst hilfreich erwiesen. Die Verfügbarkeit geeigneter Trainingsdaten stellt jedoch weiterhin ein großes Problem dar, für das häufig individuelle Lösungsansätze gefunden werden müssen
- KonferenzbeitragOn Data Spaces for Retrieval Augmented Generation(INFORMATIK 2024, 2024) Hermsen, Felix; Nitz, Lasse; Akbari Gurabi, Mehdi; Matzutt, Roman; Mandal, AvikarshaLarge Language Models (LLMs) have revolutionized knowledge retrieval from natural language queries. However, LLMs still face challenges regarding the creation of domain-specific and accurate answers. Recently, Retrieval Augmented Generation (RAG) architecture has been proposed as one approach to addressing these challenges. While current research focuses on optimizing document retrieval and augmenting the initial query accordingly, we identify untapped potentials of RAG to retrieve knowledge from heterogeneous data sources via data spaces. In this work, we investigate three conceptual integration scenarios between RAG and data spaces. Our findings indicate that given the data space extended RAG, it could provide domain-specific information retrieval with diverse data sources. However, solutions to mitigate unintended information leakage require further consideration.
- KonferenzbeitragExtending the German Labor Market Ontology with Online Data(INFORMATIK 2024, 2024) Martić, Diana; Fischer, Andreas; Dörpinghaus, JensThe overall aim of this paper is to increase the comprehensiveness of the German Labour Market Ontology (GLMO). The GLMO provides entities for qualifications, such as occupations and training programs, as well as tools and skills. However, like most knowledge graphs, the GLMO provides only partially complete relationships between entities. This, for instance, affects the mappings of related tools, skills, and qualifications. To enrich the GLMO, publicly available data from the platforms of the Federal Employment Agency are extracted and combined with the GLMO. This integration process has led to the creation of additional entity classes for occupational metadata, including activity fields or activity areas. Moreover, additional links between skills and occupations, and between related qualifications have been established.