We introduce a novel framework, termed RMGANs, that merges Relativistic Generative Adversarial Networks (RGANs) with Margin Losses. This framework capitalizes on the strengths of both RGANs’ discriminators, which assess the realism of real and fake data, and the advantages of Margin Losses, well-known for their ability to create distinct class separation. We apply this combination to the problem of semi-supervised learning (SSL). Our study delves into the architecture of RMGANs, provides a mathematical analysis to assess the impact of margin utilization on RMGAN losses, and offers guidance on choosing hyper-parameters. We also conduct experiments across the MNIST and CIFAR-10 datasets. The empirical results clearly demonstrate RMGANs’ effectiveness in achieving higher accuracy compared to the state-of-the-art work (MarginGAN) in the SSL fashion.
Recent blockchain-based systems for managing credentials show advantages over paper-based procedures. However, issuing credentials with blockchain could conflict with current management rules and policies. One of the possible conflicts is the auditability. Most blockchain-based systems for credentials focus on security, efficiency and privacy while ignoring the auditability of the system. In this paper, we propose a new system IU-TransCert for issuing, verifying, and auditing academic credentials. The system uses a new data structure named the Auditable Merkle Tree that enables credential issuance and built-in auditing capabilities. The auditable data fields can be customized to meet regulations. Credentials are published to the blockchain in the root node of the Auditable Merkle Tree, allowing access for auditors while preserving privacy. The system provides automated and transparent auditing processes for educational authorities to independently verify credentials without involving issuers. We also present a prototype to demonstrate feasibility, and a security analysis to examine protections against threats. The analysis and discussion shows that the proposed system could enhance credential privacy, efficiency, integrity, and auditability across the university ecosystem.
In the engineering domain, representing real-world objects using a body of data, called a digital twin, which is frequently updated by "live" measurements, has shown various advantages over traditional modelling and simulation techniques. Consequently, urban planners have a strong interest in digital twin technology, since it provides them with a laboratory for experimenting with data before making far-reaching decisions. Realizing these decisions involves the work of professionals in the architecture, engineering and construction (AEC) domain who nowadays collaborate via the methodology of building information modeling (BIM). At the same time, the citizen plays an integral role both in the data acquisition phase, while also being a beneficiary of the improved resource management strategies. In this paper, we present a prototype for a "digital energy twin" platform we designed in cooperation with the city of Regensburg. We show how our extensible platform design can satisfy the various requirements of multiple user groups through a series of data processing solutions and visualizations, indicating valuable design and implementation guidelines for future projects. In particular, we focus on two example use cases concerning building electricity monitoring and BIM. By implementing a flexible data processing architecture we can involve citizens in the data acquisition process, meeting the demands of modern users regarding maximum transparency in the handling of their data.
The demand for high-resolution automotive radar operating in the 77GHz band is on the rise, especially as we approach the practical implementation of autonomous driving technology. With the increasing prevalence of in-vehicle Chirp Sequence (CS) radars in the future, there is a growing concern about the potential for broadband inter-radar interference. This interference poses a significant risk, potentially leading to a higher likelihood of undetected targets. To address this problem, various algorithm-based and learning-based schemes have been proposed to suppress the inter-radar interference, e.g., iterative threshold based zero suppression method and RNN (Recurrent Neural Network) based interference suppression method. However, they only demonstrate their effectiveness based on simulation results. In this paper, we conducted a multi-radar interference experiment with up to four interference sources and compared the performance of different schemes by using the collected real data.
