Abstract: The third phase of web means semantic web requires many web pages which are annotated with metadata. Thus, a crucial question is where to acquire these metadata. In this paper we propose our approach, a semi-automatic method to annotate the texts of documents and web pages and employs with a quite comprehensive knowledge base to categorize instances with regard to ontology. The approach is evaluated against the manual annotations and one of the most popular annotation tools which works the same as our tool. The approach is implemented in .net framework and uses the WordNet for knowledge base, an annotation tool for the Semantic Web.
Abstract: Ontology is widely being used as a tool for organizing
information, creating the relation between the subjects within the
defined knowledge domain area. Various fields such as Civil,
Biology, and Management have successful integrated ontology in
decision support systems for managing domain knowledge and to
assist their decision makers. Gross pollutant traps (GPT) are devices
used in trapping and preventing large items or hazardous particles in
polluting and entering our waterways. However choosing and
determining GPT is a challenge in Malaysia as there are inadequate
GPT data repositories being captured and shared. Hence ontology is
needed to capture, organize and represent this knowledge into
meaningful information which can be contributed to the efficiency of
GPT selection in Malaysia urbanization. A GPT Ontology framework
is therefore built as the first step to capture GPT knowledge which
will then be integrated into the decision support system. This paper
will provide several examples of the GPT ontology, and explain how
it is constructed by using the Protégé tool.
Abstract: As the enormous amount of on-line text grows on the
World-Wide Web, the development of methods for automatically
summarizing this text becomes more important. The primary goal of
this research is to create an efficient tool that is able to summarize
large documents automatically. We propose an Evolving
connectionist System that is adaptive, incremental learning and
knowledge representation system that evolves its structure and
functionality. In this paper, we propose a novel approach for Part of
Speech disambiguation using a recurrent neural network, a paradigm
capable of dealing with sequential data. We observed that
connectionist approach to text summarization has a natural way of
learning grammatical structures through experience. Experimental
results show that our approach achieves acceptable performance.
Abstract: In digital signal processing it is important to
approximate multi-dimensional data by the method called rank
reduction, in which we reduce the rank of multi-dimensional data from
higher to lower. For 2-dimennsional data, singular value
decomposition (SVD) is one of the most known rank reduction
techniques. Additional, outer product expansion expanded from SVD
was proposed and implemented for multi-dimensional data, which has
been widely applied to image processing and pattern recognition.
However, the multi-dimensional outer product expansion has behavior
of great computation complex and has not orthogonally between the
expansion terms. Therefore we have proposed an alterative method,
Third-order Orthogonal Tensor Product Expansion short for 3-OTPE.
3-OTPE uses the power method instead of nonlinear optimization
method for decreasing at computing time. At the same time the group
of B. D. Lathauwer proposed Higher-Order SVD (HOSVD) that is
also developed with SVD extensions for multi-dimensional data.
3-OTPE and HOSVD are similarly on the rank reduction of
multi-dimensional data. Using these two methods we can obtain
computation results respectively, some ones are the same while some
ones are slight different. In this paper, we compare 3-OTPE to
HOSVD in accuracy of calculation and computing time of resolution,
and clarify the difference between these two methods.
Abstract: A new digital watermarking technique for images that
are sensitive to blocking artifacts is presented. Experimental results
show that the proposed MDCT based approach produces highly
imperceptible watermarked images and is robust to attacks such as
compression, noise, filtering and geometric transformations. The
proposed MDCT watermarking technique is applied to fingerprints
for ensuring security. The face image and demographic text data of
an individual are used as multiple watermarks. An AFIS system was
used to quantitatively evaluate the matching performance of the
MDCT-based watermarked fingerprint. The high fingerprint
matching scores show that the MDCT approach is resilient to
blocking artifacts. The quality of the extracted face and extracted text
images was computed using two human visual system metrics and
the results show that the image quality was high.
Abstract: In this paper we present a hybrid search algorithm for
solving constraint satisfaction and optimization problems. This
algorithm combines ideas of two basic approaches: complete and
incomplete algorithms which also known as systematic search and
local search algorithms. Different characteristics of systematic search
and local search methods are complementary. Therefore we have
tried to get the advantages of both approaches in the presented
algorithm. The major advantage of presented algorithm is finding
partial sound solution for complicated problems which their complete
solution could not be found in a reasonable time. This algorithm
results are compared with other algorithms using the well known
n-queens problem.
Abstract: Collaborative working environments for distance
education can be considered as a more generic form of contemporary
remote labs. At present, the majority of existing real laboratories are
not constructed to allow the involved participants to collaborate in
real time. To make this revolutionary learning environment possible
we must allow the different users to carry out an experiment
simultaneously. In recent times, multi-user environments are
successfully applied in many applications such as air traffic control
systems, team-oriented military systems, chat-text tools, multi-player
games etc. Thus, understanding the ideas and techniques behind these
systems could be of great importance in the contribution of ideas to
our e-learning environment for collaborative working. In this
investigation, collaborative working environments from theoretical
and practical perspectives are considered in order to build an
effective collaborative real laboratory, which allows two students or
more to conduct remote experiments at the same time as a team. In
order to achieve this goal, we have implemented distributed system
architecture, enabling students to obtain an automated help by either
a human tutor or a rule-based e-tutor.
Abstract: LABVIEW is a graphical programming language that has its roots in automation control and data acquisition. In this paper we have utilized this platform to provide a powerful toolset for process identification and control of nonlinear systems based on artificial neural networks (ANN). This tool has been applied to the monitoring and control of a lab-scale distillation column DELTALAB DC-SP. The proposed control scheme offers high speed of response for changes in set points and null stationary error for dual composition control and shows robustness in presence of externally imposed disturbance.
Abstract: A prototype for audio and video capture and compression in real time on a Linux platform has been developed. It is able to visualize both the captured and the compressed video at the same time, as well as the captured and compressed audio with the goal of comparing their quality. As it is based on free code, the final goal is to run it in an embedded system running Linux. Therefore, we would implement a node to capture and compress such multimedia information. Thus, it would be possible to consider the project within a larger one aimed at live broadcast of audio and video using a streaming server which would communicate with our node. Then, we would have a very powerful and flexible system with several practical applications.
Abstract: Computer languages are usually lumped together
into broad -paradigms-, leaving us in want of a finer classification
of kinds of language. Theories distinguishing between -genuine
differences- in language has been called for, and we propose that
such differences can be observed through a notion of expressive mode.
We outline this concept, propose how it could be operationalized and
indicate a possible context for the development of a corresponding
theory. Finally we consider a possible application in connection
with evaluation of language revision. We illustrate this with a case,
investigating possible revisions of the relational algebra in order to
overcome weaknesses of the division operator in connection with
universal queries.
Abstract: We have defined two suites of metrics, which cover
static and dynamic aspects of component assembly. The static
metrics measure complexity and criticality of component assembly,
wherein complexity is measured using Component Packing Density
and Component Interaction Density metrics. Further, four criticality
conditions namely, Link, Bridge, Inheritance and Size criticalities
have been identified and quantified. The complexity and criticality
metrics are combined to form a Triangular Metric, which can be used
to classify the type and nature of applications. Dynamic metrics are
collected during the runtime of a complete application. Dynamic
metrics are useful to identify super-component and to evaluate the
degree of utilisation of various components. In this paper both static
and dynamic metrics are evaluated using Weyuker-s set of properties.
The result shows that the metrics provide a valid means to measure
issues in component assembly. We relate our metrics suite with
McCall-s Quality Model and illustrate their impact on product
quality and to the management of component-based product
development.
Abstract: Global Software Development (GSD) projects are
passing through different boundaries of a company, country and even
in other continents where time zone differs between both sites.
Beside many benefits of such development, research declared plenty
of negative impacts on these GSD projects. It is important to
understand problems which may lie during the execution of GSD
project with different time zones. This research project discussed and
provided different issues related to time delays in GSD projects. In
this paper, authors investigated some of the time delay factors which
usually lie in GSD projects with different time zones. This
investigation is done through systematic review of literature.
Furthermore, the practices to overcome these delay factors which
have already been reported in literature and GSD organizations are
also explored through literature survey and case studies.
Abstract: With the exponential growth of networked system and
application such as eCommerce, the demand for effective internet
security is increasing. Cryptology is the science and study of systems
for secret communication. It consists of two complementary fields of
study: cryptography and cryptanalysis. The application of genetic
algorithms in the cryptanalysis of knapsack ciphers is suggested by
Spillman [7]. In order to improve the efficiency of genetic algorithm
attack on knapsack cipher, the previously published attack was
enhanced and re-implemented with variation of initial assumptions
and results are compared with Spillman results. The experimental
result of research indicates that the efficiency of genetic algorithm
attack on knapsack cipher can be improved with variation of initial
assumption.
Abstract: From a set of shifted, blurred, and decimated image , super-resolution image reconstruction can get a high-resolution image. So it has become an active research branch in the field of image restoration. In general, super-resolution image restoration is an ill-posed problem. Prior knowledge about the image can be combined to make the problem well-posed, which contributes to some regularization methods. In the regularization methods at present, however, regularization parameter was selected by experience in some cases and other techniques have too heavy computation cost for computing the parameter. In this paper, we construct a new super-resolution algorithm by transforming the solving of the System stem Є=An into the solving of the equations X+A*X-1A=I , and propose an inverse iterative method.
Abstract: Advancement in Artificial Intelligence has lead to the
developments of various “smart" devices. Character recognition
device is one of such smart devices that acquire partial human
intelligence with the ability to capture and recognize various
characters in different languages. Firstly multiscale neural training
with modifications in the input training vectors is adopted in this
paper to acquire its advantage in training higher resolution character
images. Secondly selective thresholding using minimum distance
technique is proposed to be used to increase the level of accuracy of
character recognition. A simulator program (a GUI) is designed in
such a way that the characters can be located on any spot on the
blank paper in which the characters are written. The results show that
such methods with moderate level of training epochs can produce
accuracies of at least 85% and more for handwritten upper case
English characters and numerals.
Abstract: Many researchers are working on information hiding
techniques using different ideas and areas to hide their secrete data.
This paper introduces a robust technique of hiding secret data in
image based on LSB insertion and RSA encryption technique. The
key of the proposed technique is to encrypt the secret data. Then the
encrypted data will be converted into a bit stream and divided it into
number of segments. However, the cover image will also be divided
into the same number of segments. Each segment of data will be
compared with each segment of image to find the best match
segment, in order to create a new random sequence of segments to be
inserted then in a cover image. Experimental results show that the
proposed technique has a high security level and produced better
stego-image quality.
Abstract: The necessity of solving multi dimensional
complicated scientific problems beside the necessity of several
objective functions optimization are the most motive reason of born
of artificial intelligence and heuristic methods.
In this paper, we introduce a new method for multiobjective
optimization based on learning automata. In the proposed method,
search space divides into separate hyper-cubes and each cube is
considered as an action. After gathering of all objective functions
with separate weights, the cumulative function is considered as the
fitness function. By the application of all the cubes to the cumulative
function, we calculate the amount of amplification of each action and
the algorithm continues its way to find the best solutions. In this
Method, a lateral memory is used to gather the significant points of
each iteration of the algorithm. Finally, by considering the
domination factor, pareto front is estimated. Results of several
experiments show the effectiveness of this method in comparison
with genetic algorithm based method.
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: In this paper, we will present an architecture for the
implementation of a real time stereoscopic images correction's
approach. This architecture is parallel and makes use of several
memory blocs in which are memorized pre calculated data relating to
the cameras used for the acquisition of images. The use of reduced
images proves to be essential in the proposed approach; the
suggested architecture must so be able to carry out the real time
reduction of original images.
Abstract: Modern applications realized onto FPGAs exhibit high connectivity demands. Throughout this paper we study the routing constraints of Virtex devices and we propose a systematic methodology for designing a novel general-purpose interconnection network targeting to reconfigurable architectures. This network consists of multiple segment wires and SB patterns, appropriately selected and assigned across the device. The goal of our proposed methodology is to maximize the hardware utilization of fabricated routing resources. The derived interconnection scheme is integrated on a Virtex style FPGA. This device is characterized both for its high-performance, as well as for its low-energy requirements. Due to this, the design criterion that guides our architecture selections was the minimal Energy×Delay Product (EDP). The methodology is fully-supported by three new software tools, which belong to MEANDER Design Framework. Using a typical set of MCNC benchmarks, extensive comparison study in terms of several critical parameters proves the effectiveness of the derived interconnection network. More specifically, we achieve average Energy×Delay Product reduction by 63%, performance increase by 26%, reduction in leakage power by 21%, reduction in total energy consumption by 11%, at the expense of increase of channel width by 20%.