48,289 research outputs found
On Secure Workflow Decentralisation on the Internet
Decentralised workflow management systems are a new research area, where most
work to-date has focused on the system's overall architecture. As little
attention has been given to the security aspects in such systems, we follow a
security driven approach, and consider, from the perspective of available
security building blocks, how security can be implemented and what new
opportunities are presented when empowering the decentralised environment with
modern distributed security protocols. Our research is motivated by a more
general question of how to combine the positive enablers that email exchange
enjoys, with the general benefits of workflow systems, and more specifically
with the benefits that can be introduced in a decentralised environment. This
aims to equip email users with a set of tools to manage the semantics of a
message exchange, contents, participants and their roles in the exchange in an
environment that provides inherent assurances of security and privacy. This
work is based on a survey of contemporary distributed security protocols, and
considers how these protocols could be used in implementing a distributed
workflow management system with decentralised control . We review a set of
these protocols, focusing on the required message sequences in reviewing the
protocols, and discuss how these security protocols provide the foundations for
implementing core control-flow, data, and resource patterns in a distributed
workflow environment
Workflow resource pattern modelling and visualization
Workflow patterns have been recognized as the theoretical basis to modeling recurring problems in workflow systems. A form of workflow patterns, known as the resource patterns, characterise the behaviour of resources in workflow systems. Despite the fact that many resource patterns have been discovered, people still preclude them from many workflow system implementations. One of reasons could be obscurityin the behaviour of and interaction between resources and a workflow management system. Thus, we provide a modelling and visualization approach for the resource patterns, enabling a resource behaviour modeller to intuitively see the specific resource patterns involved in the lifecycle of a workitem. We believe this research can be extended to benefit not only workflow modelling, but also other applications, such as model validation, human resource behaviour modelling, and workflow model visualization
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Privacy-preserving model learning on a blockchain network-of-networks.
ObjectiveTo facilitate clinical/genomic/biomedical research, constructing generalizable predictive models using cross-institutional methods while protecting privacy is imperative. However, state-of-the-art methods assume a "flattened" topology, while real-world research networks may consist of "network-of-networks" which can imply practical issues including training on small data for rare diseases/conditions, prioritizing locally trained models, and maintaining models for each level of the hierarchy. In this study, we focus on developing a hierarchical approach to inherit the benefits of the privacy-preserving methods, retain the advantages of adopting blockchain, and address practical concerns on a research network-of-networks.Materials and methodsWe propose a framework to combine level-wise model learning, blockchain-based model dissemination, and a novel hierarchical consensus algorithm for model ensemble. We developed an example implementation HierarchicalChain (hierarchical privacy-preserving modeling on blockchain), evaluated it on 3 healthcare/genomic datasets, as well as compared its predictive correctness, learning iteration, and execution time with a state-of-the-art method designed for flattened network topology.ResultsHierarchicalChain improves the predictive correctness for small training datasets and provides comparable correctness results with the competing method with higher learning iteration and similar per-iteration execution time, inherits the benefits of the privacy-preserving learning and advantages of blockchain technology, and immutable records models for each level.DiscussionHierarchicalChain is independent of the core privacy-preserving learning method, as well as of the underlying blockchain platform. Further studies are warranted for various types of network topology, complex data, and privacy concerns.ConclusionWe demonstrated the potential of utilizing the information from the hierarchical network-of-networks topology to improve prediction
The Algol triple system spatially resolved at optical wavelengths
Interacting binaries typically have separations in the milli-arcsecond regime
and hence it has been challenging to resolve them at any wavelength. However,
recent advances in optical interferometry have improved our ability to discern
the components in these systems and have now enabled the direct determination
of physical parameters. We used the Navy Prototype Optical Interferometer to
produce for the first time images resolving all three components in the
well-known Algol triple system. Specifically, we have separated the tertiary
component from the binary and simultaneously resolved the eclipsing binary
pair, which represents the nearest and brightest eclipsing binary in the sky.
We present revised orbital elements for the triple system, and we have
rectified the 180-degree ambiguity in the position angle of Algol C. Our
directly determined magnitude differences and masses for this triple star
system are consistent with earlier light curve modeling results.Comment: Accepted to Astrophysical Journal Letters, 15 pages, 2 eps figures
A Causal And-Or Graph Model for Visibility Fluent Reasoning in Tracking Interacting Objects
Tracking humans that are interacting with the other subjects or environment
remains unsolved in visual tracking, because the visibility of the human of
interests in videos is unknown and might vary over time. In particular, it is
still difficult for state-of-the-art human trackers to recover complete human
trajectories in crowded scenes with frequent human interactions. In this work,
we consider the visibility status of a subject as a fluent variable, whose
change is mostly attributed to the subject's interaction with the surrounding,
e.g., crossing behind another object, entering a building, or getting into a
vehicle, etc. We introduce a Causal And-Or Graph (C-AOG) to represent the
causal-effect relations between an object's visibility fluent and its
activities, and develop a probabilistic graph model to jointly reason the
visibility fluent change (e.g., from visible to invisible) and track humans in
videos. We formulate this joint task as an iterative search of a feasible
causal graph structure that enables fast search algorithm, e.g., dynamic
programming method. We apply the proposed method on challenging video sequences
to evaluate its capabilities of estimating visibility fluent changes of
subjects and tracking subjects of interests over time. Results with comparisons
demonstrate that our method outperforms the alternative trackers and can
recover complete trajectories of humans in complicated scenarios with frequent
human interactions.Comment: accepted by CVPR 201
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