Abstract: Bio-chips are used for experiments on genes and
contain various information such as genes, samples and so on. The
two-dimensional bio-chips, in which one axis represent genes and the
other represent samples, are widely being used these days. Instead of
experimenting with real genes which cost lots of money and much
time to get the results, bio-chips are being used for biological
experiments. And extracting data from the bio-chips with high
accuracy and finding out the patterns or useful information from such
data is very important. Bio-chip analysis systems extract data from
various kinds of bio-chips and mine the data in order to get useful
information. One of the commonly used methods to mine the data is
classification. The algorithm that is used to classify the data can be
various depending on the data types or number characteristics and so
on. Considering that bio-chip data is extremely large, an algorithm that
imitates the ecosystem such as the ant algorithm is suitable to use as an
algorithm for classification. This paper focuses on finding the
classification rules from the bio-chip data using the Ant Colony
algorithm which imitates the ecosystem. The developed system takes
in consideration the accuracy of the discovered rules when it applies it
to the bio-chip data in order to predict the classes.
Abstract: The paper proposes a novel technique for iris
recognition using texture and phase features. Texture features are
extracted on the normalized iris strip using Haar Wavelet while phase
features are obtained using LOG Gabor Wavelet. The matching
scores generated from individual modules are combined using sum of
score technique. The system is tested on database obtained from Bath
University and Indian Institute of Technology Kanpur and is giving
an accuracy of 95.62% and 97.66% respectively. The FAR and FRR
of the combined system is also reduced comparatively.
Abstract: In the recent past Learning Classifier Systems have
been successfully used for data mining. Learning Classifier System
(LCS) is basically a machine learning technique which combines
evolutionary computing, reinforcement learning, supervised or
unsupervised learning and heuristics to produce adaptive systems. A
LCS learns by interacting with an environment from which it
receives feedback in the form of numerical reward. Learning is
achieved by trying to maximize the amount of reward received. All
LCSs models more or less, comprise four main components; a finite
population of condition–action rules, called classifiers; the
performance component, which governs the interaction with the
environment; the credit assignment component, which distributes the
reward received from the environment to the classifiers accountable
for the rewards obtained; the discovery component, which is
responsible for discovering better rules and improving existing ones
through a genetic algorithm. The concatenate of the production rules
in the LCS form the genotype, and therefore the GA should operate
on a population of classifier systems. This approach is known as the
'Pittsburgh' Classifier Systems. Other LCS that perform their GA at
the rule level within a population are known as 'Mitchigan' Classifier
Systems. The most predominant representation of the discovered
knowledge is the standard production rules (PRs) in the form of IF P
THEN D. The PRs, however, are unable to handle exceptions and do
not exhibit variable precision. The Censored Production Rules
(CPRs), an extension of PRs, were proposed by Michalski and
Winston that exhibit variable precision and supports an efficient
mechanism for handling exceptions. A CPR is an augmented
production rule of the form: IF P THEN D UNLESS C, where
Censor C is an exception to the rule. Such rules are employed in
situations, in which conditional statement IF P THEN D holds
frequently and the assertion C holds rarely. By using a rule of this
type we are free to ignore the exception conditions, when the
resources needed to establish its presence are tight or there is simply
no information available as to whether it holds or not. Thus, the IF P
THEN D part of CPR expresses important information, while the
UNLESS C part acts only as a switch and changes the polarity of D
to ~D. In this paper Pittsburgh style LCSs approach is used for
automated discovery of CPRs. An appropriate encoding scheme is
suggested to represent a chromosome consisting of fixed size set of
CPRs. Suitable genetic operators are designed for the set of CPRs
and individual CPRs and also appropriate fitness function is proposed
that incorporates basic constraints on CPR. Experimental results are
presented to demonstrate the performance of the proposed learning
classifier system.
Abstract: In this paper, we present a novel objective nonreference
performance assessment algorithm for image fusion. It takes
into account local measurements to estimate how well the important
information in the source images is represented by the fused image.
The metric is based on the Universal Image Quality Index and uses
the similarity between blocks of pixels in the input images and the
fused image as the weighting factors for the metrics. Experimental
results confirm that the values of the proposed metrics correlate well
with the subjective quality of the fused images, giving a significant
improvement over standard measures based on mean squared error
and mutual information.
Abstract: Image-based Rendering(IBR) techniques recently
reached in broad fields which leads to a critical challenge to build up
IBR-Driven visualization platform where meets requirement of high
performance, large bounds of distributed visualization resource
aggregation and concentration, multiple operators deploying and
CSCW design employing. This paper presents an unique IBR-based
visualization dataflow model refer to specific characters of IBR
techniques and then discusses prominent feature of IBR-Driven
distributed collaborative visualization (DCV) system before finally
proposing an novel prototype. The prototype provides a well-defined
three level modules especially work as Central Visualization Server,
Local Proxy Server and Visualization Aid Environment, by which
data and control for collaboration move through them followed the
previous dataflow model. With aid of this triple hierarchy architecture
of that, IBR oriented application construction turns to be easy. The
employed augmented collaboration strategy not only achieve
convenient multiple users synchronous control and stable processing
management, but also is extendable and scalable.
Abstract: Cryptographic protocols are widely used in various
applications to provide secure communications. They are usually
represented as communicating agents that send and receive messages.
These agents use their knowledge to exchange information and
communicate with other agents involved in the protocol. An agent
knowledge can be partitioned into explicit knowledge and procedural
knowledge. The explicit knowledge refers to the set of information
which is either proper to the agent or directly obtained from other
agents through communication. The procedural knowledge relates to
the set of mechanisms used to get new information from what is
already available to the agent.
In this paper, we propose a mathematical framework which specifies
the explicit knowledge of an agent involved in a cryptographic
protocol. Modelling this knowledge is crucial for the specification,
analysis, and implementation of cryptographic protocols. We also,
report on a prototype tool that allows the representation and the
manipulation of the explicit knowledge.
Abstract: EGOTHOR is a search engine that indexes the Web
and allows us to search the Web documents. Its hit list contains URL
and title of the hits, and also some snippet which tries to shortly
show a match. The snippet can be almost always assembled by an
algorithm that has a full knowledge of the original document (mostly
HTML page). It implies that the search engine is required to store
the full text of the documents as a part of the index.
Such a requirement leads us to pick up an appropriate compression
algorithm which would reduce the space demand. One of the solutions
could be to use common compression methods, for instance gzip or
bzip2, but it might be preferable if we develop a new method which
would take advantage of the document structure, or rather, the textual
character of the documents.
There already exist a special compression text algorithms and
methods for a compression of XML documents. The aim of this
paper is an integration of the two approaches to achieve an optimal
level of the compression ratio
Abstract: The conjugate gradient optimization algorithm
usually used for nonlinear least squares is presented and is
combined with the modified back propagation algorithm yielding
a new fast training multilayer perceptron (MLP) algorithm
(CGFR/AG). The approaches presented in the paper consist of
three steps: (1) Modification on standard back propagation
algorithm by introducing gain variation term of the activation
function, (2) Calculating the gradient descent on error with
respect to the weights and gains values and (3) the determination
of the new search direction by exploiting the information
calculated by gradient descent in step (2) as well as the previous
search direction. The proposed method improved the training
efficiency of back propagation algorithm by adaptively modifying
the initial search direction. Performance of the proposed method
is demonstrated by comparing to the conjugate gradient algorithm
from neural network toolbox for the chosen benchmark. The
results show that the number of iterations required by the
proposed method to converge is less than 20% of what is required
by the standard conjugate gradient and neural network toolbox
algorithm.
Abstract: The increasing interest on processing data created by
sensor networks has evolved into approaches to implement sensor
networks as databases. The aggregation operator, which calculates a
value from a large group of data such as computing averages or sums,
etc. is an essential function that needs to be provided when
implementing such sensor network databases. This work proposes to
add the DURING clause into TinySQL to calculate values during a
specific long period and suggests a way to implement the aggregation
service in sensor networks by applying materialized view and
incremental view maintenance techniques that is used in data
warehouses. In sensor networks, data values are passed from child
nodes to parent nodes and an aggregation value is computed at the root
node. As such root nodes need to be memory efficient and low
powered, it becomes a problem to recompute aggregate values from all
past and current data. Therefore, applying incremental view
maintenance techniques can reduce the memory consumption and
support fast computation of aggregate values.