Semantic Motif Segmentation of Archaeological Fresco Fragments

With the rapid proliferation of smart grid technologies, a large amount of fine-grained power data records have been collected and stored by different parties (e.g., the power supply bureau). Pooling together the records held by the parties makes mining the data value, and therefore promises enhanced energy management and efficiency. Despite the benefits of sharing these data, it also raises concerns about data privacy and security. To this end, we present a novel approach for privacy-preserving cross-party power data sharing approach in light of the Generative Adversarial Network (named PowerGAN), which enables the involved parties to construct a shared dataset without compromising the privacy of these parties. In PowerGAN, a centralized curator is assigned a generator, while each party possesses a discriminator. The key idea of PowerGAN is to let data holders jointly train the generator held by the centralized server. In addition, to prevent the curator from inferring sensitive data about the parties, we designed a privacy-preserving RMSProp (Root Mean Square Propagation) optimizer. Furthermore, we design a dynamic noise perturbation method, which dynamically tunes the noise to further promote the utility of the final shared data. Through comprehensive privacy analysis, we show that our PowerGAN approach provides strict privacy protection. Evaluations of real-world datasets show the effectiveness of PowerGAN in addressing the privacy concerns associated with multi-party power data sharing.

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Computational techniques for virtual reconstruction of fragmented archaeological textiles

Gigilashvili,

Lukesova,

Gulbrandsen

et al. 2023

Herit Sci

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Archaeological artifacts play important role in understanding the past developments of the humanity. However, the artifacts are often highly fragmented and degraded, with many details and parts missing due to centuries’ long degradation. Archaeologists and conservators attempt to reconstruct the original state of the objects either physically or virtually. This process includes characterizing and matching fragments’ features to identify which ones belong together. However, this process currently requires an extensive and tedious manual labor. Recent development in computational techniques gave rise to computer-assisted ways of virtual reconstruction, where the computer suggests solutions to the puzzle of scattered fragments and supplements or fully replaces manual labor. However, the capabilities of computational techniques remain limited in many aspects. This review summarizes the state-of-the-art computational techniques for puzzle and virtual reconstruction problems in cultural heritage applications, in general – with a particular interest in archaeological textiles. We overview existing computational methods, their applications and limitations. Afterward, based on the current knowledge gaps, we discuss where the field should go next.

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Semantic Motif Segmentation of Archaeological Fresco Fragments

Cited by 3 publications

References 19 publications

Compact Multi-Object Placement Using Adjacency-Aware Reinforcement Learning

Compact Multi-Object Placement Using Adjacency-Aware Reinforcement Learning

Privacy-preserving multiparty power data sharing based on generative adversarial network

Computational techniques for virtual reconstruction of fragmented archaeological textiles

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