16,636 research outputs found
DRSP : Dimension Reduction For Similarity Matching And Pruning Of Time Series Data Streams
Similarity matching and join of time series data streams has gained a lot of
relevance in today's world that has large streaming data. This process finds
wide scale application in the areas of location tracking, sensor networks,
object positioning and monitoring to name a few. However, as the size of the
data stream increases, the cost involved to retain all the data in order to aid
the process of similarity matching also increases. We develop a novel framework
to addresses the following objectives. Firstly, Dimension reduction is
performed in the preprocessing stage, where large stream data is segmented and
reduced into a compact representation such that it retains all the crucial
information by a technique called Multi-level Segment Means (MSM). This reduces
the space complexity associated with the storage of large time-series data
streams. Secondly, it incorporates effective Similarity Matching technique to
analyze if the new data objects are symmetric to the existing data stream. And
finally, the Pruning Technique that filters out the pseudo data object pairs
and join only the relevant pairs. The computational cost for MSM is O(l*ni) and
the cost for pruning is O(DRF*wsize*d), where DRF is the Dimension Reduction
Factor. We have performed exhaustive experimental trials to show that the
proposed framework is both efficient and competent in comparison with earlier
works.Comment: 20 pages,8 figures, 6 Table
A hierarchical Mamdani-type fuzzy modelling approach with new training data selection and multi-objective optimisation mechanisms: A special application for the prediction of mechanical properties of alloy steels
In this paper, a systematic data-driven fuzzy modelling methodology is proposed, which allows to construct Mamdani fuzzy models considering both accuracy (precision) and transparency (interpretability) of fuzzy systems. The new methodology employs a fast hierarchical clustering algorithm to generate an initial fuzzy model efficiently; a training data selection mechanism is developed to identify appropriate and efficient data as learning samples; a high-performance Particle Swarm Optimisation (PSO) based multi-objective optimisation mechanism is developed to further improve the fuzzy model in terms of both the structure and the parameters; and a new tolerance analysis method is proposed to derive the confidence bands relating to the final elicited models. This proposed modelling approach is evaluated using two benchmark problems and is shown to outperform other modelling approaches. Furthermore, the proposed approach is successfully applied to complex high-dimensional modelling problems for manufacturing of alloy steels, using ārealā industrial data. These problems concern the prediction of the mechanical properties of alloy steels by correlating them with the heat treatment process conditions as well as the weight percentages of the chemical compositions
Audit Games with Multiple Defender Resources
Modern organizations (e.g., hospitals, social networks, government agencies)
rely heavily on audit to detect and punish insiders who inappropriately access
and disclose confidential information. Recent work on audit games models the
strategic interaction between an auditor with a single audit resource and
auditees as a Stackelberg game, augmenting associated well-studied security
games with a configurable punishment parameter. We significantly generalize
this audit game model to account for multiple audit resources where each
resource is restricted to audit a subset of all potential violations, thus
enabling application to practical auditing scenarios. We provide an FPTAS that
computes an approximately optimal solution to the resulting non-convex
optimization problem. The main technical novelty is in the design and
correctness proof of an optimization transformation that enables the
construction of this FPTAS. In addition, we experimentally demonstrate that
this transformation significantly speeds up computation of solutions for a
class of audit games and security games
On the construction of probabilistic Newton-type algorithms
It has recently been shown that many of the existing quasi-Newton algorithms
can be formulated as learning algorithms, capable of learning local models of
the cost functions. Importantly, this understanding allows us to safely start
assembling probabilistic Newton-type algorithms, applicable in situations where
we only have access to noisy observations of the cost function and its
derivatives. This is where our interest lies.
We make contributions to the use of the non-parametric and probabilistic
Gaussian process models in solving these stochastic optimisation problems.
Specifically, we present a new algorithm that unites these approximations
together with recent probabilistic line search routines to deliver a
probabilistic quasi-Newton approach.
We also show that the probabilistic optimisation algorithms deliver promising
results on challenging nonlinear system identification problems where the very
nature of the problem is such that we can only access the cost function and its
derivative via noisy observations, since there are no closed-form expressions
available
Optimizing Associative Information Transfer within Content-addressable Memory
Original article can be found at: http://www.oldcitypublishing.com/IJUC/IJUC.htmlPeer reviewe
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