10,352 research outputs found
Algorithm Selection Framework for Cyber Attack Detection
The number of cyber threats against both wired and wireless computer systems
and other components of the Internet of Things continues to increase annually.
In this work, an algorithm selection framework is employed on the NSL-KDD data
set and a novel paradigm of machine learning taxonomy is presented. The
framework uses a combination of user input and meta-features to select the best
algorithm to detect cyber attacks on a network. Performance is compared between
a rule-of-thumb strategy and a meta-learning strategy. The framework removes
the conjecture of the common trial-and-error algorithm selection method. The
framework recommends five algorithms from the taxonomy. Both strategies
recommend a high-performing algorithm, though not the best performing. The work
demonstrates the close connectedness between algorithm selection and the
taxonomy for which it is premised.Comment: 6 pages, 7 figures, 1 table, accepted to WiseML '2
Structural Analysis and Stochastic Modelling Suggest a Mechanism for Calmodulin Trapping by CaMKII
Activation of CaMKII by calmodulin and the subsequent maintenance of constitutive activity through autophosphorylation at threonine residue 286 (Thr286) are thought to play a major role in synaptic plasticity. One of the effects of autophosphorylation at Thr286 is to increase the apparent affinity of CaMKII for calmodulin, a phenomenon known as “calmodulin trapping”. It has previously been suggested that two binding sites for calmodulin exist on CaMKII, with high and low affinities, respectively. We built structural models of calmodulin bound to both of these sites. Molecular dynamics simulation showed that while binding of calmodulin to the supposed low-affinity binding site on CaMKII is compatible with closing (and hence, inactivation) of the kinase, and could even favour it, binding to the high-affinity site is not. Stochastic simulations of a biochemical model showed that the existence of two such binding sites, one of them accessible only in the active, open conformation, would be sufficient to explain calmodulin trapping by CaMKII. We can explain the effect of CaMKII autophosphorylation at Thr286 on calmodulin trapping: It stabilises the active state and therefore makes the high-affinity binding site accessible. Crucially, a model with only one binding site where calmodulin binding and CaMKII inactivation are strictly mutually exclusive cannot reproduce calmodulin trapping. One of the predictions of our study is that calmodulin binding in itself is not sufficient for CaMKII activation, although high-affinity binding of calmodulin is
Learning-Assisted Automated Reasoning with Flyspeck
The considerable mathematical knowledge encoded by the Flyspeck project is
combined with external automated theorem provers (ATPs) and machine-learning
premise selection methods trained on the proofs, producing an AI system capable
of answering a wide range of mathematical queries automatically. The
performance of this architecture is evaluated in a bootstrapping scenario
emulating the development of Flyspeck from axioms to the last theorem, each
time using only the previous theorems and proofs. It is shown that 39% of the
14185 theorems could be proved in a push-button mode (without any high-level
advice and user interaction) in 30 seconds of real time on a fourteen-CPU
workstation. The necessary work involves: (i) an implementation of sound
translations of the HOL Light logic to ATP formalisms: untyped first-order,
polymorphic typed first-order, and typed higher-order, (ii) export of the
dependency information from HOL Light and ATP proofs for the machine learners,
and (iii) choice of suitable representations and methods for learning from
previous proofs, and their integration as advisors with HOL Light. This work is
described and discussed here, and an initial analysis of the body of proofs
that were found fully automatically is provided
Middleware-based Database Replication: The Gaps between Theory and Practice
The need for high availability and performance in data management systems has
been fueling a long running interest in database replication from both academia
and industry. However, academic groups often attack replication problems in
isolation, overlooking the need for completeness in their solutions, while
commercial teams take a holistic approach that often misses opportunities for
fundamental innovation. This has created over time a gap between academic
research and industrial practice.
This paper aims to characterize the gap along three axes: performance,
availability, and administration. We build on our own experience developing and
deploying replication systems in commercial and academic settings, as well as
on a large body of prior related work. We sift through representative examples
from the last decade of open-source, academic, and commercial database
replication systems and combine this material with case studies from real
systems deployed at Fortune 500 customers. We propose two agendas, one for
academic research and one for industrial R&D, which we believe can bridge the
gap within 5-10 years. This way, we hope to both motivate and help researchers
in making the theory and practice of middleware-based database replication more
relevant to each other.Comment: 14 pages. Appears in Proc. ACM SIGMOD International Conference on
Management of Data, Vancouver, Canada, June 200
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