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Various probabilistic time series forecasting models have sprung up and shown remarkably good performance. However, the choice of model highly relies on the characteristics of the input time series and the fixed distribution that the model…
MimbleWimble (MW) is a privacy-oriented cryptocurrency technology which provides security and scalability properties that distinguish it from other protocols of its kind. We present and discuss those properties and outline the basis of a…
Despite the extent of recent advances in Machine Learning (ML) and Neural Networks, providing formal guarantees on the behavior of these systems is still an open problem, and a crucial requirement for their adoption in regulated or…
Organizations typically train large models individually. This is costly and time-consuming, particularly for large-scale foundation models. Such vertical production is known to be suboptimal. Inspired by this economic insight, we ask…
Machine Learning (ML) has emerged as one of data science's most transformative and influential domains. However, the widespread adoption of ML introduces privacy-related concerns owing to the increasing number of malicious attacks targeting…
Machine learning (ML) model trading, known for its role in protecting data privacy, faces a major challenge: information asymmetry. This issue can lead to model deception, a problem that current literature has not fully solved, where the…
Smart contracts codify real-world transactions and automatically execute the terms of the contract when predefined conditions are met. This paper proposes SmartML, a modeling language for smart contracts that is platform independent and…
Testing of machine learning (ML) models is a known challenge identified by researchers and practitioners alike. Unfortunately, current practice for ML model testing prioritizes testing for model performance, while often neglecting the…
Nowadays, the High Performance Computing is part of the context of embedded systems. Graphics Processing Units (GPUs) are more and more used in acceleration of the most part of algorithms and applications. Over the past years, not many…
Modeling distributions that depend on external control parameters is a common scenario in diverse applications like molecular simulations, where system properties like temperature affect molecular configurations. Despite the relevance of…
Federated Learning trains machine learning models on distributed devices by aggregating local model updates instead of local data. However, privacy concerns arise as the aggregated local models on the server may reveal sensitive personal…
Modern machine learning (ML) models are expensive IP and business competitiveness often depends on keeping this IP confidential. This in turn restricts how these models are deployed; for example, it is unclear how to deploy a model…
Gold and bitcoin are not new to us, but with limited cash and time, given only the past stream of the daily price of gold and bitcoin, it is a kind of new problem for us to develop a certain model and determine the best strategy to get the…
The development of Large Language Models (LLMs) has created transformative opportunities for the financial industry, especially in the area of financial trading. However, how to integrate LLMs with trading systems has become a challenge. To…
Machine Learning (ML) has become one of the most impactful fields of data science in recent years. However, a significant concern with ML is its privacy risks due to rising attacks against ML models. Privacy-Preserving Machine Learning…
Using blockchain technology, it is possible to create contracts that offer a reward in exchange for a trained machine learning model for a particular data set. This would allow users to train machine learning models for a reward in a…
Large language models (LLMs) offer personalized responses based on user interactions, but this use case raises serious privacy concerns. Homomorphic encryption (HE) is a cryptographic protocol supporting arithmetic computations in encrypted…
This paper discusses how model checking, a technique used for the verification of behavioural requirements of dynamic systems, can be usefully deployed for the verification of contracts. A process view of agreements between parties is…
When neural network model and data are outsourced to cloud server for inference, it is desired to preserve the confidentiality of model and data as the involved parties (i.e., cloud server, model providing client and data providing client)…
Ethereum clients execute transactions in a sequential order prescribed by the consensus protocol. This is a safe and conservative approach to blockchain transaction processing which forgoes running transactions in parallel even when doing…