Abstract

Abstract

Abstract
The increasing number of IoT devices and digital services offers cross-domain sensing and control opportunities to a growing set of stakeholders. The provision of cross-domain digital services requires interoperability as a key enabler to bridge domain specifics, while inferring knowledge and allowing new data-driven services. This work addresses H2020 InterConnect project's Interoperability Framework, highlighting the use of semantic web technologies. The interoperability framework layering is presented, particularly addressing the Semantic Interoperability layer as its cornerstone to build an interoperable ecosystem of cross-domain digital services via a federation of distributed knowledge bases. Departing from a generic, ontology-agnostic approach that can fit any cross-domain use case, it validates the approach by considering the SAREF family of ontologies, showcasing an IoT and energy cross-domain use case.

Abstract

Abstract
With continuous technological advancement, multihomed devices are becoming common. They can connect simultaneously to multiple networks through different interfaces. However, since TCP sessions are bound to one interface per device, it hampers applications from taking advantage of all the available connected networks. This has been solved by MPTCP, introduced as a seamless extension to TCP, allowing more reliable sessions and enhanced throughput. However, MPTCP comes with an inherent risk, as it becomes easier to fragment attacks towards evading NIDS. This paper presents a study of how MPTCP can be used to evade NIDS through simple cross-path attacks. It also introduces tools to facilitate assessing MPTCP-based services in diverse network topologies using an emulation environment. Finally, a new solution is proposed to prevent cross-path attacks through uncoordinated networks. This solution consists of a hostlevel plugin that allows MPTCP sessions only through trusted networks, even in the presence of a NAT.

Abstract
This article proposes a Markov Chain Monte Carlo (MCMC) rendering algorithm based on a family of guided transition kernels. The kernels exploit properties of ensembles of light transport paths, which are distributed according to the lighting in the scene, and utilize this information to make informed decisions for guiding local path sampling. Critically, our approach does not require caching distributions in world space, saving time and memory, yet it is able to make guided sampling decisions based on whole paths. We show how this can be implemented efficiently by organizing the paths in each ensemble and designing transition kernels for MCMC rendering based on a carefully chosen subset of paths from the ensemble. This algorithm is easy to parallelize and leads to improvements in variance when rendering a variety of scenes.