Distributed constraint optimization (DCOP) is a framework in which multiple agents with private constraints (or preferences) cooperate to achieve a common goal optimally. DCOPs are applicable in several multi-agent coordination/allocation problems, such as ...
Magnetic resonance spectroscopy is a powerful, non-invasive, quantitative imaging technique that allows for the measurement of brain metabolites that has demonstrated utility in diagnosing and characterizing a broad range of neurological diseases. Its impa ...
The transition towards a human-centered indoor climate is beneficial from occupants’ thermal comfort and from an energy reduction perspective. However, achieving this goal requires the knowledge of the thermal state of individuals at the level of body part ...
A multi-agent system consists of a collection of decision-making or learning agents subjected to streaming observations from some real-world phenomenon. The goal of the system is to solve some global learning or optimization problem in a distributed or dec ...
Background: Increasingly, hospitals and research institutes are developing technical solutions for sharing patient data in a privacy preserving manner. Two of these technical solutions are homomorphic encryption and distributed ledger technology. Homomorph ...
Some governance functions traditionally performed by humans are increasingly informed and sometimes automatically executed by machine learning algorithms (governance by machine learning) to benefit society. Therefore, it is necessary to think also about th ...
Synthetic data has been advertised as a silver-bullet solution to privacy-preserving data publishing that addresses the shortcomings of traditional anonymisation techniques. The promise is that synthetic data drawn from generative models preserves the stat ...
This contribution outlines a research proposal combining ethical guidelines on AI and a law-as-data approach. Building upon the definitions of soft law discussed in legal scholarship, it proposes a way of structuring the regulatory landscape on AI and of a ...
Although decoding the content of mental states is currently unachievable, technologies such as neural interfaces, affective computing systems, and digital behavioral technologies enable increasingly reliable statistical associations between certain data pa ...
Analyzing and processing data that are siloed and dispersed among multiple distrustful stakeholders is difficult and can even become impossible when the data are sensitive or confidential. Current data-protection and privacy regulations highly restrict the ...
