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: In this paper we discuss the effect of unbounded particle interaction operator on particle growth and we study how this can address the choice of appropriate time steps of the numerical simulation. We provide also rigorous mathematical proofs showing that large particles become dominating with increasing time while small particles contribute negligibly. Second, we discuss the efficiency of the algorithm by performing numerical simulations tests and by comparing the simulated solutions with some known analytic solutions to the Smoluchowski equation.
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: 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: As the Internet continues to grow at a rapid pace as
the primary medium for communications and commerce and as
telecommunication networks and systems continue to expand their
global reach, digital information has become the most popular and
important information resource and our dependence upon the
underlying cyber infrastructure has been increasing significantly.
Unfortunately, as our dependency has grown, so has the threat to the
cyber infrastructure from spammers, attackers and criminal
enterprises. In this paper, we propose a new machine learning based
network intrusion detection framework for cyber security. The
detection process of the framework consists of two stages: model
construction and intrusion detection. In the model construction stage,
a semi-supervised machine learning algorithm is applied to a
collected set of network audit data to generate a profile of normal
network behavior and in the intrusion detection stage, input network
events are analyzed and compared with the patterns gathered in the
profile, and some of them are then flagged as anomalies should these
events are sufficiently far from the expected normal behavior. The
proposed framework is particularly applicable to the situations where
there is only a small amount of labeled network training data
available, which is very typical in real world network environments.
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: 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: The development of shape and size of a crack in a
pressure vessel under uniaxial and biaxial loadings is important in
fitness-for-service evaluations such as leak-before-break. In this
work finite element modelling was used to evaluate the mean stress
and the J-integral around a front of a surface-breaking crack. A
procedure on the basis of ductile tearing resistance curves of high and
low constrained fracture mechanics geometries was developed to
estimate the amount of ductile crack extension for surface-breaking
cracks and to show the evolution of the initial crack shape. The
results showed non-uniform constraint levels and crack driving forces
around the crack front at large deformation levels. It was also shown
that initially semi-elliptical surface cracks under biaxial load
developed higher constraint levels around the crack front than in
uniaxial tension. However similar crack shapes were observed with
more extensions associated with cracks under biaxial loading.
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: Feature selection study is gaining importance due to its contribution to save classification cost in terms of time and computation load. In search of essential features, one of the methods to search the features is via the decision tree. Decision tree act as an intermediate feature space inducer in order to choose essential features. In decision tree-based feature selection, some studies used decision tree as a feature ranker with a direct threshold measure, while others remain the decision tree but utilized pruning condition that act as a threshold mechanism to choose features. This paper proposed threshold measure using Manhattan Hierarchical Cluster distance to be utilized in feature ranking in order to choose relevant features as part of the feature selection process. The result is promising, and this method can be improved in the future by including test cases of a higher number of attributes.
Abstract: The creation of a sustainable future depends on the knowledge and involvement of the people, as well as an understanding of the consequences of individual actions. Construction industry has long been associated with the detrimental effects to our mother earth. In Malaysia, the government, professional bodies and private companies are beginning to take heed in the necessity to reduce this environmental problem without restraining the need for development. This paper focuses on the actions undertaken by the Malaysian government, non-government organizations and construction players in promoting sustainability in construction. To ensure that those concerted efforts are not only skin deep in its impact, a survey was conducted to investigate the awareness of the developers regarding this issue and whether those developers has absorb the concept of sustainable construction in their current practices. The survey revealed that although the developers are aware of the rising issues on sustainability, little efforts are generated from them in implementing it. More effort is necessary to boost this application and further stimulate actions and strategies towards a sustainable built environment.
Abstract: XML has become a popular standard for information exchange via web. Each XML document can be presented as a rooted, ordered, labeled tree. The Node label shows the exact position of a node in the original document. Region and Dewey encoding are two famous methods of labeling trees. In this paper, we propose a new insert friendly labeling method named IFDewey based on recently proposed scheme, called Extended Dewey. In Extended Dewey many labels must be modified when a new node is inserted into the XML tree. Our method eliminates this problem by reserving even numbers for future insertion. Numbers generated by Extended Dewey may be even or odd. IFDewey modifies Extended Dewey so that only odd numbers are generated and even numbers can then be used for a much easier insertion of nodes.
Abstract: We present a simplified equalization technique for a
π/4 differential quadrature phase shift keying ( π/4 -DQPSK) modulated
signal in a multipath fading environment. The proposed equalizer is
realized as a fractionally spaced adaptive decision feedback equalizer
(FS-ADFE), employing exponential step-size least mean square
(LMS) algorithm as the adaptation technique. The main advantage of
the scheme stems from the usage of exponential step-size LMS algorithm
in the equalizer, which achieves similar convergence behavior
as that of a recursive least squares (RLS) algorithm with significantly
reduced computational complexity. To investigate the finite-precision
performance of the proposed equalizer along with the π/4 -DQPSK
modem, the entire system is evaluated on a 16-bit fixed point digital
signal processor (DSP) environment. The proposed scheme is found
to be attractive even for those cases where equalization is to be
performed within a restricted number of training samples.
Abstract: With the enormous growth on the web, users get easily
lost in the rich hyper structure. Thus developing user friendly and
automated tools for providing relevant information without any
redundant links to the users to cater to their needs is the primary task
for the website owners. Most of the existing web mining algorithms
have concentrated on finding frequent patterns while neglecting the
less frequent one that are likely to contain the outlying data such as
noise, irrelevant and redundant data. This paper proposes new
algorithm for mining the web content by detecting the redundant
links from the web documents using set theoretical(classical
mathematics) such as subset, union, intersection etc,. Then the
redundant links is removed from the original web content to get the
required information by the user..
Abstract: In this paper, we propose a reversible watermarking
scheme based on histogram shifting (HS) to embed watermark bits
into the H.264/AVC standard videos by modifying the last nonzero
level in the context adaptive variable length coding (CAVLC) domain.
The proposed method collects all of the last nonzero coefficients (or
called last level coefficient) of 4×4 sub-macro blocks in a macro
block and utilizes predictions for the current last level from the
neighbor block-s last levels to embed watermark bits. The feature of
the proposed method is low computational and has the ability of
reversible recovery. The experimental results have demonstrated that
our proposed scheme has acceptable degradation on video quality and
output bit-rate for most test videos.
Abstract: Classification of video sequences based on their contents is a vital process for adaptation techniques. It helps decide which adaptation technique best fits the resource reduction requested by the client. In this paper we used the principal feature analysis algorithm to select a reduced subset of video features. The main idea is to select only one feature from each class based on the similarities between the features within that class. Our results showed that using this feature reduction technique the source video features can be completely omitted from future classification of video sequences.
Abstract: Tofurther advance research on immune-related genes
from T. molitor, we constructed acDNA library and analyzed
expressed sequence taq (EST) sequences from 1,056 clones. After
removing vector sequence and quality checkingthrough thePhred
program (trim_alt 0.05 (P-score>20), 1039 sequences were generated.
The average length of insert was 792 bp. In addition, we identified 162
clusters, 167 contigs and 391 contigs after clustering and assembling
process using a TGICL package. EST sequences were searchedagainst
NCBI nr database by local BLAST (blastx, E
Abstract: The use of machine vision to inspect the outcome of
surgical tasks is investigated, with the aim of incorporating this
approach in robotic surgery systems. Machine vision is a non-contact
form of inspection i.e. no part of the vision system is in direct contact
with the patient, and is therefore well suited for surgery where
sterility is an important consideration,. As a proof-of-concept, three
primary surgical tasks for a common neurosurgical procedure were
inspected using machine vision. Experiments were performed on
cadaveric pig heads to simulate the two possible outcomes i.e.
satisfactory or unsatisfactory, for tasks involved in making a burr
hole, namely incision, retraction, and drilling. We identify low level
image features to distinguish the two outcomes, as well as report on
results that validate our proposed approach. The potential of using
machine vision in a surgical environment, and the challenges that
must be addressed, are identified and discussed.
Abstract: This paper outlines the development of a learning retrieval agent. Task of this agent is to extract knowledge of the Active Semantic Network in respect to user-requests. Based on a reinforcement learning approach, the agent learns to interpret the user-s intention. Especially, the learning algorithm focuses on the retrieval of complex long distant relations. Increasing its learnt knowledge with every request-result-evaluation sequence, the agent enhances his capability in finding the intended information.
Abstract: This work presents the results of a study carried out to
determine the sliding wear behavior and its effect on the process
parameters of components manufactured by direct metal laser
sintering (DMLS). A standard procedure and specimen had been used
in the present study to find the wear behavior. Using Taguchi-s
experimental technique, an orthogonal array of modified L8 had been
developed. Sliding wear testing using pin-on-disk machine was
carried out and analysis of variance (ANOVA) technique was used to
investigate the effect of process parameters and to identify the main
process parameter that influences the properties of wear behavior on
the DMLS components. It has been found that part orientation, one
of the selected process parameter had more influence on wear as
compared to other selected process parameters.