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Multiple RADIO home deployments, healthcare services providers, and medical researchers make up an ecosystem where different actors have different access rights to the data collected by RADIO monitoring. This chapter presents the two different access modes and technologies that support them: access controls to individual users’ data for the purposes of medical monitoring and privacy-aware aggregation for the purposes of medical research.

We define a novel, extensional, three-valued semantics for higher-order logic programs with negation. The new semantics is based on interpreting the types of the source language as three-valued Fitting-monotonic functions at all levels of the type hierarchy. We prove that there exists a bijection between such Fitting-monotonic functions and pairs of two-valued-result functions where the first member of the pair is monotone-antimonotone and the second member is antimonotone-monotone. By deriving an extension of consistent approximation fixpoint theory (Denecker et al. 2004) and utilizing the above bijection, we define an iterative procedure that produces for any given higher-order logic program a distinguished extensional model. We demonstrate that this model is actually a minimal one. Moreover, we prove that our construction generalizes the familiar well-founded semantics for classical logic programs, making in this way our proposal an appealing formulation for capturing the well-founded semantics for higher-order logic programs. This paper is under consideration for acceptance in TPLP., Comment: Paper presented at the 34nd International Conference on Logic Programming (ICLP 2018), Oxford, UK, July 14 to July 17, 2018 31 pages, LaTeX

Dataset description vocabularies focus on provenance, ver-sioning, licensing, and similar metadata. VoID is a notable exception, providing some expressivity for describing subsets and their contents and can, to some extent, be used for discovering relevant resources and for optimizing querying. In this paper we describe the Sevod vocabulary, an extension of VoID that provides the expressivity needed in order to support the query planning methods typically used in federated querying. We also present a tool for automatically scraping such metadata from RDF dumps and give statistics about the size of the descriptions for the FedBench datasets.

This paper presents work in progress towards developing a new benchmark for federated query processing systems. Unlike other popular benchmarks, our queryset is not driven by technical evaluation, but is derived from workflows established by the pharmacology community. The value of this queryset is that it is realistic but at the same time it comprises complex queries that test all features of modern query processing systems.

Part 2: Special Session on Privacy Aware Machine Learning for Health Data Science (PAML 2016); International audience; The insights gained by the large-scale analysis of health-related data can have an enormous impact in public health and medical research, but access to such personal and sensitive data poses serious privacy implications for the data provider and a heavy data security and administrative burden on the data consumer. In this paper we present an architecture that fills the gap between the statistical tools ubiquitously used in medical research on the one hand, and privacy-preserving data mining methods on the other. This architecture foresees the primitive instructions needed to re-implement the elementary statistical methods so that they only access data via a privacy-preserving protocol. The advantage is that more complex analysis and visualisation tools that are built upon these elementary methods can remain unaffected. Furthermore, we introduce RASSP, a secure summation protocol that implements the primitive instructions foreseen by the architecture. An open-source reference implementation of this architecture is provided for the R language. We use these results to argue that the tension between medical research and privacy requirements can be technically alleviated and we outline a research plan towards a system that covers further requirements on computation efficiency and on the trust that the medical researcher can place on the statistical results obtained by it.

Extensional higher-order logic programming has been introduced as a generalization of classical logic programming. An important characteristic of this paradigm is that it preserves all the well-known properties of traditional logic programming. In this paper we consider the semantics of negation in the context of the new paradigm. Using some recent results from non-monotonic fixed-point theory, we demonstrate that every higher-order logic program with negation has a unique minimum infinite-valued model. In this way we obtain the first purely model-theoretic semantics for negation in extensional higher-order logic programming. Using our approach, we resolve an old paradox that was introduced by W. W. Wadge in order to demonstrate the semantic difficulties of higher-order logic programming.

We propose a purely extensional semantics for higher-order logic programming. In this semantics program predicates denote sets of ordered tuples, and two predicates are equal iff they are equal as sets. Moreover, every program has a unique minimum Herbrand model which is the greatest lower bound of all Herbrand models of the program and the least fixed-point of an immediate consequence operator. We also propose an SLD-resolution proof procedure which is proven sound and complete with respect to the minimum model semantics. In other words, we provide a purely extensional theoretical framework for higher-order logic programming which generalizes the familiar theory of classical (first-order) logic programming., 45 pages

The DARE platform has been designed to help research developers deliver user-facing applications and solutions over diverse underlying e-infrastructures, data and computational contexts. The platform is Cloud-ready, and relies on the exposure of APIs, which are suitable for raising the abstraction level and hiding complexity. At its core, the platform implements the cataloguing and execution of fine-grained and Python-based dispel4py workflows as services. Reflection is achieved via a logical knowledge base, comprising multiple internal catalogues, registries and semantics, while it supports persistent and pervasive data provenance. This paper presents design and implementation aspects of the DARE platform, as well as it provides directions for future development.