Fraunhofer Institute for Open Communication Systems

International research estimates that digital video piracy inflicts up to $71 billion in annual losses on the global entertainment industry [1]. Digital Rights Management (DRM) is a key technology to control and manage how digital video is accessed and used, helping to protect the intellectual property of content creators.

The next generation of wireless mobile networks will be driven by software and infrastructure virtualization. At the same time, the underlying hardware will become more diverse. Edge, intermediate and central network nodes will allow distributed deployments of never seen before scale. Non-terrestrial networks, nomadic networks and networks in networks promise more dynamicity, changing their topologies over time in predictable and unpredictable ways. To handle the complexity and dynamicity, a highly flexible and adaptable network is required. The key enablers of this complex digital communication system will be programmable infrastructures. From programmable access and flexible backhauls to zero-touch, adaptable and resilient control planes, we conducted research into advanced programmability at different layers of the network, within the Open6GHub project. In this article, we present innovative solutions for advanced programmable network infrastructure and show why they are essential for our vision of the programmable infrastructure of future wireless mobile networks.

Abstract As part of its commitment to innovation and to improve the accessibility of the library’s collection of resources from the long nineteenth century, the British Library ran the “Living with Machines” project between 2018 and 2023. The Library’s project uses Artificial Intelligence (AI) technologies to improve access to historical collections. The library faced the challenge of making the extensive collections easier to interact and more accessible to users. The project was initiated to explore how AI and machine learning (ML) would impact historical research, with a particular focus on the impact of mechanisation in the nineteenth century. The project team was gathered from 2018 to July 2019. The project involved over 5500 volunteers and engaged the public through crowdsourcing tasks. The collaboration spanned various disciplines, including data scientists, historians and research software developers. The British Library also reached out to the public to ask for their help in commenting on the artefacts. Collaboration challenges arose when staff did not spend enough time understanding each other’s disciplines. According to the interviewees, librarians will need a solid grounding in “information organisation” when it comes to working with AI solutions and outputs.

Abstract The National Library of Finland developed and implemented an automated subject indexing and classification tool, Annif, which is now also used by other National Libraries. The tool is integrated into Finto AI, an in-house metadata management system launched in 2020. These two AI-driven solutions were created by the growing volume of digitally collected data, the extensive digitisation of paper archives, inadequate AI support for various Finnish languages, and a lack of transparency in the services offered by external providers on the market. The successful implementation of Annif and Finto AI can be attributed to the strong collaboration between the project team and their external stakeholders, such as other National Libraries or universities, as well as the awareness within the Finnish National Library. Use cases, for example, from the University of Jyväskylä, highlight the value of these AI-driven solutions. However, challenges remain, such as identifying hidden errors in the automatically generated records. Fortunately, the National Library’s extensive in-house expertise allows for the immediate detection of mistakes in both the code and the generated records.

This paper investigates neural network-based bio-metric authentication using finger-drawn digits on touchscreen devices. We evaluated CNN and auto encoder architectures for user authentication through simple digit patterns (0–9) traced with finger input. Twenty participants contributed 2,000 finger-drawn digits each on personal touchscreen devices. We compared two CNN architectures: a modified Inception-V1 network and a lightweight shallow CNN for mobile environments. Additionally, we examined Convolutional and Fully Connected autoencoders for anomaly detection. Both CNN architectures achieved ~89% authentication accuracy, with the shallow CNN requiring fewer parameters. Autoencoder approaches achieved ~75% accuracy. The results demonstrate that finger-drawn symbol authentication provides a viable, secure, and user-friendly biometric solution for touchscreen devices. This approach can be integrated with existing pattern-based authentication methods to create multi-layered security systems for mobile applications.

Privacy is an important aspect of smart parking services provided by open urban platforms, because these systems process personal data such as license plates. In this work, we present a privacy-preserving computer vision system for a specific use-case of detecting unpaid parked cars. The main contribution is a system-level demonstration of an existing homomorphic encryption protocol CKKS applied to the parking lot surveillance. The use of homomorphic encryption enables encrypted license plate data processing on an external open urban platform without disclosing the specific content of the plates to platform owners. System implementation includes a convolutional neural network that operates on encrypted data. We evaluate the system experimentally and show that it reaches <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$84,27 {\%}$</tex> accuracy in license plate recognition and scales linearly with number of cars on a parking space and number of requests albeit with a slow processing time per request due to computational costs associated with homomorphic encryption.

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This study explores 'anthrogames', purpose-built games that merge anthropological knowledge with interactive design. Analyzing three pioneering examples, it traces anthropology's evolving engagement with games: from virtual ethnographies and studies of game production to creating purpose-built games. The authors present a novel theoretical framework distinguishing anthrogames from educational games and traditional ethnographic media. Drawing on computer science, anthropology, and game studies, the study identifies design patterns and methodologies that translate ethnographic insights into engaging, scholarly sound gameplay. It highlights the potential of anthrogames to redefine cultural representation through emerging technologies like Large Language Models and collaborative design. Positioned at the intersection of ethnography, technology, and participatory design, anthrogames challenge anthropologists and designers to develop innovative frameworks for evaluating and creating cultural representation in interactive media. This research underscores their transformative potential in computational anthropology and interactive ethnography, bridging scholarship and player engagement.