70,738 research outputs found
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Certifying Services in Cloud: The Case for a Hybrid, Incremental and Multi-layer Approach
The use of clouds raises significant security concerns for the services they provide. Addressing these concerns requires novel models of cloud service certification based on multiple forms of evidence including testing and monitoring data, and trusted computing proofs. CUMULUS is a novel infrastructure for realising such certification models
Hybrid Deterministic-Stochastic Methods for Data Fitting
Many structured data-fitting applications require the solution of an
optimization problem involving a sum over a potentially large number of
measurements. Incremental gradient algorithms offer inexpensive iterations by
sampling a subset of the terms in the sum. These methods can make great
progress initially, but often slow as they approach a solution. In contrast,
full-gradient methods achieve steady convergence at the expense of evaluating
the full objective and gradient on each iteration. We explore hybrid methods
that exhibit the benefits of both approaches. Rate-of-convergence analysis
shows that by controlling the sample size in an incremental gradient algorithm,
it is possible to maintain the steady convergence rates of full-gradient
methods. We detail a practical quasi-Newton implementation based on this
approach. Numerical experiments illustrate its potential benefits.Comment: 26 pages. Revised proofs of Theorems 2.6 and 3.1, results unchange
Global Trajectory Optimisation : Can We Prune the Solution Space When Considering Deep Space Manoeuvres? [Final Report]
This document contains a report on the work done under the ESA/Ariadna study 06/4101 on the global optimization of space trajectories with multiple gravity assist (GA) and deep space manoeuvres (DSM). The study was performed by a joint team of scientists from the University of Reading and the University of Glasgow
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An intelligent system for risk classification of stock investment projects
The proposed paper demonstrates that a hybrid fuzzy neural network can serve as a risk classifier of stock investment projects. The training algorithm for the regular part of the network is based on bidirectional incremental evolution proving more efficient than direct evolution. The approach is compared with other crisp and soft investment appraisal and trading techniques, while building a multimodel domain representation for an intelligent decision support system. Thus the advantages of each model are utilised while looking at the investment problem from different perspectives. The empirical results are based on UK companies traded on the London Stock Exchange
Battery Degradation Maps for Power System Optimization and as a Benchmark Reference
This paper presents a novel method to describe battery degradation. We use
the concept of degradation maps to model the incremental charge capacity loss
as a function of discrete battery control actions and state of charge. The maps
can be scaled to represent any battery system in size and power. Their convex
piece-wise affine representations allow for tractable optimal control
formulations and can be used in power system simulations to incorporate battery
degradation. The map parameters for different battery technologies are
published making them an useful basis to benchmark different battery
technologies in case studies
Fitting Jump Models
We describe a new framework for fitting jump models to a sequence of data.
The key idea is to alternate between minimizing a loss function to fit multiple
model parameters, and minimizing a discrete loss function to determine which
set of model parameters is active at each data point. The framework is quite
general and encompasses popular classes of models, such as hidden Markov models
and piecewise affine models. The shape of the chosen loss functions to minimize
determine the shape of the resulting jump model.Comment: Accepted for publication in Automatic
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Hybrid process modelling within business process management projects
Business Process Management (BPM) is still an important research topic amongst both academics
and businesses. The recent recession has forced businesses to focus on cost control and efficiency
in order to better cope with the economic downturn. Many companies in this situation turn to BPM
software as a means of improving their efficiency and costs by reducing aspects of the business
such as process lead-times and material costs. In order to identify areas of the business and its
processes which require changing the business will most likely adopt a method of modelling their
business processes. Because of the large number of available techniques decision makers usually
struggle to decide the best approach. Recent literature has also pointed out that prevalent
modelling techniques are designed to serve one specific purpose and may not be capable of
modelling the whole picture. The key relationship between the information systems and the human
behaviour is one example of where existing techniques are biased towards opposite ends of the
scale. This paper proposes the use of a hybrid modelling notation composed of multiple existing
notations in order to bridge this. The hybrid notation was applied to a BPM project at a company
in the construction industry and a case study conducted with its users
Speculative Approximations for Terascale Analytics
Model calibration is a major challenge faced by the plethora of statistical
analytics packages that are increasingly used in Big Data applications.
Identifying the optimal model parameters is a time-consuming process that has
to be executed from scratch for every dataset/model combination even by
experienced data scientists. We argue that the incapacity to evaluate multiple
parameter configurations simultaneously and the lack of support to quickly
identify sub-optimal configurations are the principal causes. In this paper, we
develop two database-inspired techniques for efficient model calibration.
Speculative parameter testing applies advanced parallel multi-query processing
methods to evaluate several configurations concurrently. The number of
configurations is determined adaptively at runtime, while the configurations
themselves are extracted from a distribution that is continuously learned
following a Bayesian process. Online aggregation is applied to identify
sub-optimal configurations early in the processing by incrementally sampling
the training dataset and estimating the objective function corresponding to
each configuration. We design concurrent online aggregation estimators and
define halting conditions to accurately and timely stop the execution. We apply
the proposed techniques to distributed gradient descent optimization -- batch
and incremental -- for support vector machines and logistic regression models.
We implement the resulting solutions in GLADE PF-OLA -- a state-of-the-art Big
Data analytics system -- and evaluate their performance over terascale-size
synthetic and real datasets. The results confirm that as many as 32
configurations can be evaluated concurrently almost as fast as one, while
sub-optimal configurations are detected accurately in as little as a
fraction of the time
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