Search Results for author: Michael Huth

Found 7 papers, 0 papers with code

Market Misconduct in Decentralized Finance (DeFi): Analysis, Regulatory Challenges and Policy Implications

no code implementations29 Nov 2023 Xihan Xiong, Zhipeng Wang, Tianxiang Cui, William Knottenbelt, Michael Huth

The rise of blockchain and Decentralized Finance (DeFi) underscores this intertwined evolution of technology and finance.

Leverage Staking with Liquid Staking Derivatives (LSDs): Opportunities and Risks

no code implementations28 Nov 2023 Xihan Xiong, Zhipeng Wang, Xi Chen, William Knottenbelt, Michael Huth

Lido, the leading Liquid Staking Derivative (LSD) provider on Ethereum, allows users to stake an arbitrary amount of ETH to receive stETH, which can be integrated with Decentralized Finance (DeFi) protocols such as Aave.

Secure Bayesian Federated Analytics for Privacy-Preserving Trend Detection

no code implementations28 Jul 2021 Amit Chaulwar, Michael Huth

Federated analytics has many applications in edge computing, its use can lead to better decision making for service provision, product development, and user experience.

Decision Making Edge-computing +1

Ontology-Based Reasoning about the Trustworthiness of Cyber-Physical Systems

no code implementations20 Mar 2018 Marcello Balduccini, Edward Griffor, Michael Huth, Claire Vishik, Martin Burns, David Wollman

The example analyzed in the paper demonstrates the enrichment of the original CPS model obtained through ontology and reasoning and its ability to deliver additional insights to the developers and operators of CPS.

Constrained Bayesian Networks: Theory, Optimization, and Applications

no code implementations15 May 2017 Paul Beaumont, Michael Huth

We develop the theory and practice of an approach to modelling and probabilistic inference in causal networks that is suitable when application-specific or analysis-specific constraints should inform such inference or when little or no data for the learning of causal network structure or probability values at nodes are available.

Bayesian Inference

Manyopt: An Extensible Tool for Mixed, Non-Linear Optimization Through SMT Solving

no code implementations4 Feb 2017 Andrea Callia D'Iddio, Michael Huth

We therefore present a tool, ManyOpt, for MINLP optimization that enables experimentation with reduction techniques which transform a MINLP problem to feasibility checking realized by an SMT solver.

Chemical Process Management

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