4,826 research outputs found
An Intelligent QoS Identification for Untrustworthy Web Services Via Two-phase Neural Networks
QoS identification for untrustworthy Web services is critical in QoS
management in the service computing since the performance of untrustworthy Web
services may result in QoS downgrade. The key issue is to intelligently learn
the characteristics of trustworthy Web services from different QoS levels, then
to identify the untrustworthy ones according to the characteristics of QoS
metrics. As one of the intelligent identification approaches, deep neural
network has emerged as a powerful technique in recent years. In this paper, we
propose a novel two-phase neural network model to identify the untrustworthy
Web services. In the first phase, Web services are collected from the published
QoS dataset. Then, we design a feedforward neural network model to build the
classifier for Web services with different QoS levels. In the second phase, we
employ a probabilistic neural network (PNN) model to identify the untrustworthy
Web services from each classification. The experimental results show the
proposed approach has 90.5% identification ratio far higher than other
competing approaches.Comment: 8 pages, 5 figure
How to Reuse and Compose Knowledge for a Lifetime of Tasks: A Survey on Continual Learning and Functional Composition
A major goal of artificial intelligence (AI) is to create an agent capable of
acquiring a general understanding of the world. Such an agent would require the
ability to continually accumulate and build upon its knowledge as it encounters
new experiences. Lifelong or continual learning addresses this setting, whereby
an agent faces a continual stream of problems and must strive to capture the
knowledge necessary for solving each new task it encounters. If the agent is
capable of accumulating knowledge in some form of compositional representation,
it could then selectively reuse and combine relevant pieces of knowledge to
construct novel solutions. Despite the intuitive appeal of this simple idea,
the literatures on lifelong learning and compositional learning have proceeded
largely separately. In an effort to promote developments that bridge between
the two fields, this article surveys their respective research landscapes and
discusses existing and future connections between them
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