Abstract: This paper introduces a temporal epistemic logic
CBCTL that updates agent-s belief states through communications
in them, based on computational tree logic (CTL). In practical
environments, communication channels between agents may not be
secure, and in bad cases agents might suffer blackouts. In this study,
we provide inform* protocol based on ACL of FIPA, and declare the
presence of secure channels between two agents, dependent on time.
Thus, the belief state of each agent is updated along with the progress
of time. We show a prover, that is a reasoning system for a given
formula in a given a situation of an agent ; if it is directly provable
or if it could be validated through the chains of communications, the
system returns the proof.
Abstract: Statistical learning theory was developed by Vapnik. It
is a learning theory based on Vapnik-Chervonenkis dimension. It also
has been used in learning models as good analytical tools. In general, a
learning theory has had several problems. Some of them are local
optima and over-fitting problems. As well, statistical learning theory
has same problems because the kernel type, kernel parameters, and
regularization constant C are determined subjectively by the art of
researchers. So, we propose an evolutionary statistical learning theory
to settle the problems of original statistical learning theory.
Combining evolutionary computing into statistical learning theory,
our theory is constructed. We verify improved performances of an
evolutionary statistical learning theory using data sets from KDD cup.
Abstract: This paper presents a new steganography approach suitable for Arabic texts. It can be classified under steganography feature coding methods. The approach hides secret information bits within the letters benefiting from their inherited points. To note the specific letters holding secret bits, the scheme considers the two features, the existence of the points in the letters and the redundant Arabic extension character. We use the pointed letters with extension to hold the secret bit 'one' and the un-pointed letters with extension to hold 'zero'. This steganography technique is found attractive to other languages having similar texts to Arabic such as Persian and Urdu.
Abstract: This paper presents a simplified version of Data Envelopment Analysis (DEA) - a conventional approach to evaluating the performance and ranking of competitive objects characterized by two groups of factors acting in opposite directions: inputs and outputs. DEA with a Perfect Object (DEA PO) augments the group of actual objects with a virtual Perfect Object - the one having greatest outputs and smallest inputs. It allows for obtaining an explicit analytical solution and making a step to an absolute efficiency. This paper develops this approach further and introduces a DEA model with Partially Perfect Objects. DEA PPO consecutively eliminates the smallest relative inputs or greatest relative outputs, and applies DEA PO to the reduced collections of indicators. The partial efficiency scores are combined to get the weighted efficiency score. The computational scheme remains simple, like that of DEA PO, but the advantage of the DEA PPO is taking into account all of the inputs and outputs for each actual object. Firm evaluation is considered as an example.
Abstract: This paper proposes a method, combining color and
layout features, for identifying documents captured from lowresolution
handheld devices. On one hand, the document image color
density surface is estimated and represented with an equivalent
ellipse and on the other hand, the document shallow layout structure
is computed and hierarchically represented. The combined color and
layout features are arranged in a symbolic file, which is unique for
each document and is called the document-s visual signature. Our
identification method first uses the color information in the
signatures in order to focus the search space on documents having a
similar color distribution, and finally selects the document having the
most similar layout structure in the remaining search space. Finally,
our experiment considers slide documents, which are often captured
using handheld devices.
Abstract: Text categorization is the problem of classifying text
documents into a set of predefined classes. In this paper, we
investigated three approaches to build a meta-classifier in order to
increase the classification accuracy. The basic idea is to learn a metaclassifier
to optimally select the best component classifier for each
data point. The experimental results show that combining classifiers
can significantly improve the accuracy of classification and that our
meta-classification strategy gives better results than each individual
classifier. For 7083 Reuters text documents we obtained a
classification accuracies up to 92.04%.
Abstract: Histogram equalization is often used in image enhancement, but it can be also used in auto exposure. However, conventional histogram equalization does not work well when many pixels are concentrated in a narrow luminance range.This paper proposes an auto exposure method based on 2-way histogram equalization. Two cumulative distribution functions are used, where one is from dark to bright and the other is from bright to dark. In this paper, the proposed auto exposure method is also designed and implemented for image signal processors with full-HD images.
Abstract: Parallel programming models exist as an abstraction
of hardware and memory architectures. There are several parallel
programming models in commonly use; they are shared memory
model, thread model, message passing model, data parallel model,
hybrid model, Flynn-s models, embarrassingly parallel computations
model, pipelined computations model. These models are not specific
to a particular type of machine or memory architecture. This paper
expresses the model program for concurrent approach to data parallel
model through java programming.
Abstract: One of the most ancient humankind concerns is knowledge formalization i.e. what a concept is. Concept Analysis, a branch of analytical philosophy, relies on the purpose of decompose the elements, relations and meanings of a concept. This paper aims at presenting a method to make a concept analysis obtaining a knowledge representation suitable to be processed by a computer system using either object-oriented or ontology technologies. Security notion is, usually, known as a set of different concepts related to “some kind of protection". Our method concludes that a more general framework for the concept, despite it is dynamic, is possible and any particular definition (instantiation) depends on the elements used by its construction instead of the concept itself.
Abstract: CIM is the standard formalism for modeling management
information developed by the Distributed Management Task
Force (DMTF) in the context of its WBEM proposal, designed to
provide a conceptual view of the managed environment. In this
paper, we propose the inclusion of formal knowledge representation
techniques, based on Description Logics (DLs) and the Web Ontology
Language (OWL), in CIM-based conceptual modeling, and then we
examine the benefits of such a decision. The proposal is specified as a
CIM metamodel level mapping to a highly expressive subset of DLs
capable of capturing all the semantics of the models. The paper shows
how the proposed mapping can be used for automatic reasoning
about the management information models, as a design aid, by means
of new-generation CASE tools, thanks to the use of state-of-the-art
automatic reasoning systems that support the proposed logic and use
algorithms that are sound and complete with respect to the semantics.
Such a CASE tool framework has been developed by the authors and
its architecture is also introduced. The proposed formalization is not
only useful at design time, but also at run time through the use of
rational autonomous agents, in response to a need recently recognized
by the DMTF.
Abstract: Facial features are frequently used to represent local
properties of a human face image in computer vision applications. In
this paper, we present a fast algorithm that can extract the facial
features online such that they can give a satisfying representation of a
face image. It includes one step for a coarse detection of each facial
feature by AdaBoost and another one to increase the accuracy of the
found points by Active Shape Models (ASM) in the regions of interest.
The resulted facial features are evaluated by matching with artificial
face models in the applications of physiognomy. The distance measure
between the features and those in the fate models from the database is
carried out by means of the Hausdorff distance. In the experiment, the
proposed method shows the efficient performance in facial feature
extractions and online system of physiognomy.
Abstract: The need to have standards has always been a priority
of all the disciplines in the world. Today, standards such as XML and
USB are trying to create a universal interface for their respective
areas. The information regarding every family in the discipline
addressed, must have a lot in common, known as Metadata. A lot of
work has been done in specific domains such as IEEE LOM and
MPEG-7 but they do not appeal to the universality of creating
Metadata for all entities, where we take an entity (object) as, not
restricted to Software Terms. This paper tries to address this problem
of universal Metadata Definition which may lead to increase in
precision of search.
Abstract: The tagging data of (users, tags and resources) constitutes a folksonomy that is the user-driven and bottom-up approach to organizing and classifying information on the Web. Tagging data stored in the folksonomy include a lot of very useful information and knowledge. However, appropriate approach for analyzing tagging data and discovering hidden knowledge from them still remains one of the main problems on the folksonomy mining researches. In this paper, we have proposed a folksonomy data mining approach based on FCA for discovering hidden knowledge easily from folksonomy. Also we have demonstrated how our proposed approach can be applied in the collaborative tagging system through our experiment. Our proposed approach can be applied to some interesting areas such as social network analysis, semantic web mining and so on.
Abstract: The internet has become an attractive avenue for
global e-business, e-learning, knowledge sharing, etc. Due to
continuous increase in the volume of web content, it is not practically
possible for a user to extract information by browsing and integrating
data from a huge amount of web sources retrieved by the existing
search engines. The semantic web technology enables advancement
in information extraction by providing a suite of tools to integrate
data from different sources. To take full advantage of semantic web,
it is necessary to annotate existing web pages into semantic web
pages. This research develops a tool, named OWIE (Ontology-based
Web Information Extraction), for semantic web annotation using
domain specific ontologies. The tool automatically extracts
information from html pages with the help of pre-defined ontologies
and gives them semantic representation. Two case studies have been
conducted to analyze the accuracy of OWIE.
Abstract: Genetic Folding (GF) a new class of EA named as is
introduced for the first time. It is based on chromosomes composed
of floating genes structurally organized in a parent form and
separated by dots. Although, the genotype/phenotype system of GF
generates a kernel expression, which is the objective function of
superior classifier. In this work the question of the satisfying
mapping-s rules in evolving populations is addressed by analyzing
populations undergoing either Mercer-s or none Mercer-s rule. The
results presented here show that populations undergoing Mercer-s
rules improve practically models selection of Support Vector
Machine (SVM). The experiment is trained multi-classification
problem and tested on nonlinear Ionosphere dataset. The target of this
paper is to answer the question of evolving Mercer-s rule in SVM
addressed using either genetic folding satisfied kernel-s rules or not
applied to complicated domains and problems.
Abstract: This paper presents an improved image segmentation
model with edge preserving regularization based on the
piecewise-smooth Mumford-Shah functional. A level set formulation
is considered for the Mumford-Shah functional minimization in
segmentation, and the corresponding partial difference equations are
solved by the backward Euler discretization. Aiming at encouraging
edge preserving regularization, a new edge indicator function is
introduced at level set frame. In which all the grid points which is used
to locate the level set curve are considered to avoid blurring the edges
and a nonlinear smooth constraint function as regularization term is
applied to smooth the image in the isophote direction instead of the
gradient direction. In implementation, some strategies such as a new
scheme for extension of u+ and u- computation of the grid points and
speedup of the convergence are studied to improve the efficacy of the
algorithm. The resulting algorithm has been implemented and
compared with the previous methods, and has been proved efficiently
by several cases.
Abstract: Hybrid algorithm is the hot issue in Computational
Intelligence (CI) study. From in-depth discussion on Simulation
Mechanism Based (SMB) classification method and composite patterns,
this paper presents the Mamdani model based Adaptive Neural
Fuzzy Inference System (M-ANFIS) and weight updating formula in
consideration with qualitative representation of inference consequent
parts in fuzzy neural networks. M-ANFIS model adopts Mamdani
fuzzy inference system which has advantages in consequent part.
Experiment results of applying M-ANFIS to evaluate traffic Level
of service show that M-ANFIS, as a new hybrid algorithm in computational
intelligence, has great advantages in non-linear modeling,
membership functions in consequent parts, scale of training data and
amount of adjusted parameters.
Abstract: Heterogeneity has to be taken into account when
integrating a set of existing information sources into a distributed
information system that are nowadays often based on Service-
Oriented Architectures (SOA). This is also particularly applicable to
distributed services such as event monitoring, which are useful in the
context of Event Driven Architectures (EDA) and Complex Event
Processing (CEP). Web services deal with this heterogeneity at a
technical level, also providing little support for event processing. Our
central thesis is that such a fully generic solution cannot provide
complete support for event monitoring; instead, source specific
semantics such as certain event types or support for certain event
monitoring techniques have to be taken into account. Our core result
is the design of a configurable event monitoring (Web) service that
allows us to trade genericity for the exploitation of source specific
characteristics. It thus delivers results for the areas of SOA, Web
services, CEP and EDA.
Abstract: DNA microarrays allow the measurement of expression levels for a large number of genes, perhaps all genes of an organism, within a number of different experimental samples. It is very much important to extract biologically meaningful information from this huge amount of expression data to know the current state of the cell because most cellular processes are regulated by changes in gene expression. Association rule mining techniques are helpful to find association relationship between genes. Numerous association rule mining algorithms have been developed to analyze and associate this huge amount of gene expression data. This paper focuses on some of the popular association rule mining algorithms developed to analyze gene expression data.
Abstract: This paper looks into areas not covered by prominent
Agent-Oriented Software Engineering (AOSE) methodologies.
Extensive paper review led to the identification of two issues, first
most of these methodologies almost neglect semantic web and
ontology. Second, as expected, each one has its strength and
weakness and may focus on some phases of the development
lifecycle but not all of the phases. The work presented here builds
extensions to a highly regarded AOSE methodology (MaSE) in order
to cover the areas that this methodology does not concentrate on. The
extensions include introducing an ontology stage for semantic
representation and integrating early requirement specification from a
methodology which mainly focuses on that. The integration involved
developing transformation rules (with the necessary handling of nonmatching
notions) between the two sets of representations and
building the software which automates the transformation. The
application of this integration on a case study is also presented in the
paper. The main flow of MaSE stages was changed to smoothly
accommodate the new additions.