237 research outputs found
Dynamic Data-Driven Digital Twins for Blockchain Systems
In recent years, we have seen an increase in the adoption of blockchain-based
systems in non-financial applications, looking to benefit from what the
technology has to offer. Although many fields have managed to include
blockchain in their core functionalities, the adoption of blockchain, in
general, is constrained by the so-called trilemma trade-off between
decentralization, scalability, and security. In our previous work, we have
shown that using a digital twin for dynamically managing blockchain systems
during runtime can be effective in managing the trilemma trade-off. Our Digital
Twin leverages DDDAS feedback loop, which is responsible for getting the data
from the system to the digital twin, conducting optimisation, and updating the
physical system. This paper examines how leveraging DDDAS feedback loop can
support the optimisation component of the trilemma benefiting from
Reinforcement Learning agents and a simulation component to augment the quality
of the learned model while reducing the computational overhead required for
decision-making.Comment: 10 Pages, 5 Figures accepted for publication in
InfoSymbiotics/Dynamic Data Driven Applications Systems (DDDAS2022
Scalable RDF Data Compression using X10
The Semantic Web comprises enormous volumes of semi-structured data elements.
For interoperability, these elements are represented by long strings. Such
representations are not efficient for the purposes of Semantic Web applications
that perform computations over large volumes of information. A typical method
for alleviating the impact of this problem is through the use of compression
methods that produce more compact representations of the data. The use of
dictionary encoding for this purpose is particularly prevalent in Semantic Web
database systems. However, centralized implementations present performance
bottlenecks, giving rise to the need for scalable, efficient distributed
encoding schemes. In this paper, we describe an encoding implementation based
on the asynchronous partitioned global address space (APGAS) parallel
programming model. We evaluate performance on a cluster of up to 384 cores and
datasets of up to 11 billion triples (1.9 TB). Compared to the state-of-art
MapReduce algorithm, we demonstrate a speedup of 2.6-7.4x and excellent
scalability. These results illustrate the strong potential of the APGAS model
for efficient implementation of dictionary encoding and contributes to the
engineering of larger scale Semantic Web applications
Allelic polymorphism of Ovar-DRB1 exon2 gene and parasite resistance in two dairy sheep breeds
The Ovar-DRB1 gene locus is one of the most polymorphic genes of the Major Histocompatibility Complex (Ovar-MHC) and holds a functional role to antigen presentation. The aim of this study was: a) to describe the Ovar-DRB1 locus variability in two dairy Greek sheep breeds and b) to investigate associations between this variability with resistance to gastrointestinal parasitosis. Blood and faecal samples were collected from 231 and 201 animals of Arta and Kalarrytiko breeds, respectively. The identification of alleles was performed using the sequence–base method. Faecal egg counting (FEC) of the gastrointestinal parasites and measures of blood plasma pepsinogen levels were performed in order to evaluate parasitological parameters. From this study in the overall examined animals, thirty-nine Ovar-DRB1 alleles were identified, among them, ten new alleles, reported for the first time in the literature. In Arta breed a total of twenty-four alleles were found. Among the detected alleles, ten were breed specific and five were new. Regarding the Kalarrytiko breed, twenty-nine alleles were found, fifteen of them were unique and nine were new. The studied breeds differed in their allelic profile, with only 12 common from the total of 134 different recorded genotypes. A higher number of animals with high parasitic load and high plasma pepsinogen values were found in Kalarrytiko. Associations between Ovar-DRB1 alleles with FEC values were found with certain heterozygous genotypes to present significantly reduced FEC values. The large number of detected alleles with low frequencies and the fact that the majority of animals were heterozygous, make hard to find strong association
Explainable Human-in-the-loop Dynamic Data-Driven Digital Twins
Digital Twins (DT) are essentially Dynamic Data-driven models that serve as
real-time symbiotic "virtual replicas" of real-world systems. DT can leverage
fundamentals of Dynamic Data-Driven Applications Systems (DDDAS) bidirectional
symbiotic sensing feedback loops for its continuous updates. Sensing loops can
consequently steer measurement, analysis and reconfiguration aimed at more
accurate modelling and analysis in DT. The reconfiguration decisions can be
autonomous or interactive, keeping human-in-the-loop. The trustworthiness of
these decisions can be hindered by inadequate explainability of the rationale,
and utility gained in implementing the decision for the given situation among
alternatives. Additionally, different decision-making algorithms and models
have varying complexity, quality and can result in different utility gained for
the model. The inadequacy of explainability can limit the extent to which
humans can evaluate the decisions, often leading to updates which are unfit for
the given situation, erroneous, compromising the overall accuracy of the model.
The novel contribution of this paper is an approach to harnessing
explainability in human-in-the-loop DDDAS and DT systems, leveraging
bidirectional symbiotic sensing feedback. The approach utilises interpretable
machine learning and goal modelling to explainability, and considers trade-off
analysis of utility gained. We use examples from smart warehousing to
demonstrate the approach.Comment: 10 pages, 1 figure, submitted to the 4th International Conference on
InfoSymbiotics/Dynamic Data Driven Applications Systems (DDDAS2022
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