Abstract: The understanding of the system level of biological behavior and phenomenon variously needs some elements such as gene sequence, protein structure, gene functions and metabolic pathways. Challenging problems are representing, learning and reasoning about these biochemical reactions, gene and protein structure, genotype and relation between the phenotype, and expression system on those interactions. The goal of our work is to understand the behaviors of the interactions networks and to model their evolution in time and in space. We propose in this study an ontological meta-model for the knowledge representation of the genetic regulatory networks. Ontology in artificial intelligence means the fundamental categories and relations that provide a framework for knowledge models. Domain ontology's are now commonly used to enable heterogeneous information resources, such as knowledge-based systems, to communicate with each other. The interest of our model is to represent the spatial, temporal and spatio-temporal knowledge. We validated our propositions in the genetic regulatory network of the Aarbidosis thaliana flower
Abstract: Knowledge bases are basic components of expert
systems or intelligent computational programs. Knowledge bases
provide knowledge, events that serve deduction activity,
computation and control. Therefore, researching and developing of
models for knowledge representation play an important role in
computer science, especially in Artificial Intelligence Science and
intelligent educational software. In this paper, the extensive
deduction computational model is proposed to design knowledge
bases whose attributes are able to be real values or functional values.
The system can also solve problems based on knowledge bases.
Moreover, the models and algorithms are applied to produce the
educational software for solving alternating current problems or
solving set of equations automatically.
Abstract: In the artificial intelligence field, knowledge
representation and reasoning are important areas for intelligent
systems, especially knowledge base systems and expert systems.
Knowledge representation Methods has an important role in
designing the systems. There have been many models for knowledge
such as semantic networks, conceptual graphs, and neural networks.
These models are useful tools to design intelligent systems. However,
they are not suitable to represent knowledge in the domains of reality
applications. In this paper, new models for knowledge representation
called computational networks will be presented. They have been
used in designing some knowledge base systems in education for
solving problems such as the system that supports studying
knowledge and solving analytic geometry problems, the program for
studying and solving problems in Plane Geometry, the program for
solving problems about alternating current in physics.
Abstract: There exists an injective, information-preserving function
that maps a semantic network (i.e a directed labeled network)
to a directed network (i.e. a directed unlabeled network). The edge
label in the semantic network is represented as a topological feature
of the directed network. Also, there exists an injective function that
maps a directed network to an undirected network (i.e. an undirected
unlabeled network). The edge directionality in the directed network
is represented as a topological feature of the undirected network.
Through function composition, there exists an injective function that
maps a semantic network to an undirected network. Thus, aside from
space constraints, the semantic network construct does not have any
modeling functionality that is not possible with either a directed
or undirected network representation. Two proofs of this idea will
be presented. The first is a proof of the aforementioned function
composition concept. The second is a simpler proof involving an
undirected binary encoding of a semantic network.
Abstract: Fractional Fourier Transform is a powerful tool,
which is a generalization of the classical Fourier Transform. This
paper provides a mathematical relation relating the span in Fractional
Fourier domain with the amplitude and phase functions of the signal,
which is further used to study the variation of quality factor with
different values of the transform order. It is seen that with the
increase in the number of transients in the signal, the deviation of
average Fractional Fourier span from the frequency bandwidth
increases. Also, with the increase in the transient nature of the signal,
the optimum value of transform order can be estimated based on the
quality factor variation, and this value is found to be very close to
that for which one can obtain the most compact representation. With
the entire mathematical analysis and experimentation, we consolidate
the fact that Fractional Fourier Transform gives more optimal
representations for a number of transform orders than Fourier
transform.
Abstract: Electronic Systems are the core of everyday lives.
They form an integral part in financial networks, mass transit,
telephone systems, power plants and personal computers. Electronic
systems are increasingly based on complex VLSI (Very Large Scale
Integration) integrated circuits. Initial electronic design automation is
concerned with the design and production of VLSI systems. The next
important step in creating a VLSI circuit is Physical Design. The
input to the physical design is a logical representation of the system
under design. The output of this step is the layout of a physical
package that optimally or near optimally realizes the logical
representation. Physical design problems are combinatorial in nature
and of large problem sizes. Darwin observed that, as variations are
introduced into a population with each new generation, the less-fit
individuals tend to extinct in the competition of basic necessities.
This survival of fittest principle leads to evolution in species. The
objective of the Genetic Algorithms (GA) is to find an optimal
solution to a problem .Since GA-s are heuristic procedures that can
function as optimizers, they are not guaranteed to find the optimum,
but are able to find acceptable solutions for a wide range of
problems. This survey paper aims at a study on Efficient Algorithms
for VLSI Physical design and observes the common traits of the
superior contributions.
Abstract: The paper contains an investigation of zeros Of Bargmann analytic representation. A brief introduction to Harmonic oscillator formalism is given. The Bargmann analytic representation has been studied. The zeros of Bargmann analytic function are considered. The Q or Husimi functions are introduced. The The Bargmann functions and the Husimi functions have the same zeros. The Bargmann functions f(z) have exactly q zeros. The evolution time of the zeros μn are discussed. Various examples have been given.
Abstract: Knowledge sharing in general and the contextual
access to knowledge in particular, still represent a key challenge in
the knowledge management framework. Researchers on semantic
web and human machine interface study techniques to enhance this
access. For instance, in semantic web, the information retrieval is
based on domain ontology. In human machine interface, keeping
track of user's activity provides some elements of the context that can
guide the access to information. We suggest an approach based on
these two key guidelines, whilst avoiding some of their weaknesses.
The approach permits a representation of both the context and the
design rationale of a project for an efficient access to knowledge. In
fact, the method consists of an information retrieval environment
that, in the one hand, can infer knowledge, modeled as a semantic
network, and on the other hand, is based on the context and the
objectives of a specific activity (the design). The environment we
defined can also be used to gather similar project elements in order to
build classifications of tasks, problems, arguments, etc. produced in a
company. These classifications can show the evolution of design
strategies in the company.
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: This paper describes a code clone visualization method, called FC graph, and the implementation issues. Code clone detection tools usually show the results in a textual representation. If the results are large, it makes a problem to software maintainers with understanding them. One of the approaches to overcome the situation is visualization of code clone detection results. A scatter plot is a popular approach to the visualization. However, it represents only one-to-one correspondence and it is difficult to find correspondence of code clones over multiple files. FC graph represents correspondence among files, code clones and packages in Java. All nodes in FC graph are positioned using force-directed graph layout, which is dynami- cally calculated to adjust the distances of nodes until stabilizing them. We applied FC graph to some open source programs and visualized the results. In the author’s experience, FC graph is helpful to grasp correspondence of code clones over multiple files and also code clones with in a file.
Abstract: Knowledge discovery from text and ontology learning
are relatively new fields. However their usage is extended in many
fields like Information Retrieval (IR) and its related domains. Human
Plausible Reasoning based (HPR) IR systems for example need a
knowledge base as their underlying system which is currently made
by hand. In this paper we propose an architecture based on ontology
learning methods to automatically generate the needed HPR
knowledge base.
Abstract: Sparse representation has long been studied and several
dictionary learning methods have been proposed. The dictionary
learning methods are widely used because they are adaptive. In this
paper, a new dictionary learning method for audio is proposed. Signals
are at first decomposed into different degrees of Intrinsic Mode
Functions (IMF) using Empirical Mode Decomposition (EMD)
technique. Then these IMFs form a learned dictionary. To reduce the
size of the dictionary, the K-means method is applied to the dictionary
to generate a K-EMD dictionary. Compared to K-SVD algorithm, the
K-EMD dictionary decomposes audio signals into structured
components, thus the sparsity of the representation is increased by
34.4% and the SNR of the recovered audio signals is increased by
20.9%.
Abstract: In this paper, we propose an improved 3D star skeleton
technique, which is a suitable skeletonization for human posture representation
and reflects the 3D information of human posture.
Moreover, the proposed technique is simple and then can be performed
in real-time. The existing skeleton construction techniques, such as
distance transformation, Voronoi diagram, and thinning, focus on the
precision of skeleton information. Therefore, those techniques are not
applicable to real-time posture recognition since they are computationally
expensive and highly susceptible to noise of boundary. Although
a 2D star skeleton was proposed to complement these problems,
it also has some limitations to describe the 3D information of the
posture. To represent human posture effectively, the constructed skeleton
should consider the 3D information of posture. The proposed 3D
star skeleton contains 3D data of human, and focuses on human action
and posture recognition. Our 3D star skeleton uses the 8 projection
maps which have 2D silhouette information and depth data of human
surface. And the extremal points can be extracted as the features of 3D
star skeleton, without searching whole boundary of object. Therefore,
on execution time, our 3D star skeleton is faster than the “greedy" 3D
star skeleton using the whole boundary points on the surface. Moreover,
our method can offer more accurate skeleton of posture than the
existing star skeleton since the 3D data for the object is concerned.
Additionally, we make a codebook, a collection of representative 3D
star skeletons about 7 postures, to recognize what posture of constructed
skeleton is.
Abstract: Apart from geometry, functionality is one of the most
significant hallmarks of a product. The functionality of a product can
be considered as the fundamental justification for a product
existence. Therefore a functional analysis including a complete and
reliable descriptor has a high potential to improve product
development process in various fields especially in knowledge-based
design. One of the important applications of the functional analysis
and indexing is in retrieval and design reuse concept. More than 75%
of design activity for a new product development contains reusing
earlier and existing design know-how. Thus, analysis and
categorization of product functions concluded by functional
indexing, influences directly in design optimization. This paper
elucidates and evaluates major classes for functional analysis by
discussing their major methods. Moreover it is finalized by
presenting a noble hybrid approach for functional analysis.
Abstract: A cancelable palmprint authentication system
proposed in this paper is specifically designed to overcome the
limitations of the contemporary biometric authentication system. In
this proposed system, Geometric and pseudo Zernike moments are
employed as feature extractors to transform palmprint image into a
lower dimensional compact feature representation. Before moment
computation, wavelet transform is adopted to decompose palmprint
image into lower resolution and dimensional frequency subbands.
This reduces the computational load of moment calculation
drastically. The generated wavelet-moment based feature
representation is used to generate cancelable verification key with a
set of random data. This private binary key can be canceled and
replaced. Besides that, this key also possesses high data capture
offset tolerance, with highly correlated bit strings for intra-class
population. This property allows a clear separation of the genuine
and imposter populations, as well as zero Equal Error Rate
achievement, which is hardly gained in the conventional biometric
based authentication system.
Abstract: The rapid growth of e-Commerce services is
significantly observed in the past decade. However, the method to
verify the authenticated users still widely depends on numeric
approaches. A new search on other verification methods suitable for
online e-Commerce is an interesting issue. In this paper, a new online
signature-verification method using angular transformation is
presented. Delay shifts existing in online signatures are estimated by
the estimation method relying on angle representation. In the
proposed signature-verification algorithm, all components of input
signature are extracted by considering the discontinuous break points
on the stream of angular values. Then the estimated delay shift is
captured by comparing with the selected reference signature and the
error matching can be computed as a main feature used for verifying
process. The threshold offsets are calculated by two types of error
characteristics of the signature verification problem, False Rejection
Rate (FRR) and False Acceptance Rate (FAR). The level of these two
error rates depends on the decision threshold chosen whose value is
such as to realize the Equal Error Rate (EER; FAR = FRR). The
experimental results show that through the simple programming,
employed on Internet for demonstrating e-Commerce services, the
proposed method can provide 95.39% correct verifications and 7%
better than DP matching based signature-verification method. In
addition, the signature verification with extracting components
provides more reliable results than using a whole decision making.
Abstract: Earlier studies in kinship networks have primarily
focused on observing the social relationships existing between family
relatives. In this study, we pre-identified hubs in the network to
investigate if they could play a catalyst role in the transfer of physical
information. We conducted a case study of a ceremony performed in
one of the families of a small Hindu community – the Uttar Rarhi
Kayasthas. Individuals (n = 168) who resided in 11 geographically
dispersed regions were contacted through our hub-based
representation. We found that using this representation, over 98% of
the individuals were successfully contacted within the stipulated
period. The network also demonstrated a small-world property, with
an average geodesic distance of 3.56.
Abstract: Many experimental results suggest that more precise spike timing is significant in neural information processing. We construct a self-organization model using the spatiotemporal pat-terns, where Spike-Timing Dependent Plasticity (STDP) tunes the conduction delays between neurons. We show that, for highly syn-chronized inputs, the fluctuation of conduction delays causes globally continuous and locally distributed firing patterns through the self-organization.
Abstract: The weight constrained shortest path problem
(WCSPP) is one of most several known basic problems in
combinatorial optimization. Because of its importance in many areas
of applications such as computer science, engineering and operations
research, many researchers have extensively studied the WCSPP.
This paper mainly concentrates on the reduction of total search space
for finding WCSP using some existing Genetic Algorithm (GA). For
this purpose, some controlled schemes of genetic operators are
adopted on list chromosome representation. This approach gives a
near optimum solution with smaller elapsed generation than classical
GA technique. From further analysis on the matter, a new
generalized schema theorem is also developed from the philosophy
of Holland-s theorem.
Abstract: The design of a complete expansion that allows for
compact representation of certain relevant classes of signals is a
central problem in signal processing applications. Achieving such a
representation means knowing the signal features for the purpose of
denoising, classification, interpolation and forecasting. Multilayer
Neural Networks are relatively a new class of techniques that are
mathematically proven to approximate any continuous function
arbitrarily well. Radial Basis Function Networks, which make use of
Gaussian activation function, are also shown to be a universal
approximator. In this age of ever-increasing digitization in the
storage, processing, analysis and communication of information,
there are numerous examples of applications where one needs to
construct a continuously defined function or numerical algorithm to
approximate, represent and reconstruct the given discrete data of a
signal. Many a times one wishes to manipulate the data in a way that
requires information not included explicitly in the data, which is
done through interpolation and/or extrapolation.
Tidal data are a very perfect example of time series and many
statistical techniques have been applied for tidal data analysis and
representation. ANN is recent addition to such techniques. In the
present paper we describe the time series representation capabilities
of a special type of ANN- Radial Basis Function networks and
present the results of tidal data representation using RBF. Tidal data
analysis & representation is one of the important requirements in
marine science for forecasting.