Abstract: A scalable QoS aware multicast deployment in
DiffServ networks has become an important research dimension in
recent years. Although multicasting and differentiated services are
two complementary technologies, the integration of the two
technologies is a non-trivial task due to architectural conflicts
between them. A popular solution proposed is to extend the
functionality of the DiffServ components to support multicasting. In
this paper, we propose an algorithm to construct an efficient QoSdriven
multicast tree, taking into account the available bandwidth per
service class. We also present an efficient way to provision the
limited available bandwidth for supporting heterogeneous users. The
proposed mechanism is evaluated using simulated tests. The
simulated result reveals that our algorithm can effectively minimize
the bandwidth use and transmission cost
Abstract: Current image-based individual human recognition
methods, such as fingerprints, face, or iris biometric modalities
generally require a cooperative subject, views from certain aspects,
and physical contact or close proximity. These methods cannot
reliably recognize non-cooperating individuals at a distance in the
real world under changing environmental conditions. Gait, which
concerns recognizing individuals by the way they walk, is a relatively
new biometric without these disadvantages. The inherent gait
characteristic of an individual makes it irreplaceable and useful in
visual surveillance.
In this paper, an efficient gait recognition system for human
identification by extracting two features namely width vector of
the binary silhouette and the MPEG-7-based region-based shape
descriptors is proposed. In the proposed method, foreground objects
i.e., human and other moving objects are extracted by estimating
background information by a Gaussian Mixture Model (GMM) and
subsequently, median filtering operation is performed for removing
noises in the background subtracted image. A moving target classification
algorithm is used to separate human being (i.e., pedestrian)
from other foreground objects (viz., vehicles). Shape and boundary
information is used in the moving target classification algorithm.
Subsequently, width vector of the outer contour of binary silhouette
and the MPEG-7 Angular Radial Transform coefficients are taken as
the feature vector. Next, the Principal Component Analysis (PCA)
is applied to the selected feature vector to reduce its dimensionality.
These extracted feature vectors are used to train an Hidden Markov
Model (HMM) for identification of some individuals. The proposed
system is evaluated using some gait sequences and the experimental
results show the efficacy of the proposed algorithm.
Abstract: In this paper, Tobephobia (TBP) alludes to the fear of
failure experienced by teachers to manage curriculum change. TBP is
an emerging concept and it extends the boundaries of research in
terms of how we view achievement and failure in education.
Outcomes-based education (OBE) was introduced fifteen years ago
in South African schools without simultaneously upgrading teachers-
professional competencies. This exploratory research, therefore
examines a simple question: What is the impact of TBP and OBE on
teachers? Teacher ineptitude to cope with the OBE curriculum in the
classroom is a serious problem affecting large numbers of South
African teachers. This exploratory study sought to determine the
perceived negative impact of OBE and TBP on teachers. A survey
was conducted amongst 311 teachers in Port Elizabeth and Durban,
South Africa. The results confirm the very negative impact of TBP
and OBE on teachers. This exploratory study authenticates the
existence of TBP.
Abstract: This study demonstrates the use of Class F fly ash in
combination with lime or lime kiln dust in the full depth reclamation
(FDR) of asphalt pavements. FDR, in the context of this paper, is a
process of pulverizing a predetermined amount of flexible pavement
that is structurally deficient, blending it with chemical additives and
water, and compacting it in place to construct a new stabilized base
course. Test sections of two structurally deficient asphalt pavements
were reclaimed using Class F fly ash in combination with lime and
lime kiln dust. In addition, control sections were constructed using
cement, cement and emulsion, lime kiln dust and emulsion, and mill
and fill. The service performance and structural behavior of the FDR
pavement test sections were monitored to determine how the fly ash
sections compared to other more traditional pavement rehabilitation
techniques. Service performance and structural behavior were
determined with the use of sensors embedded in the road and Falling
Weight Deflectometer (FWD) tests. Monitoring results of the FWD
tests conducted up to 2 years after reclamation show that the cement,
fly ash+LKD, and fly ash+lime sections exhibited two year resilient
modulus values comparable to open graded cement stabilized
aggregates (more than 750 ksi). The cement treatment resulted in a
significant increase in resilient modulus within 3 weeks of
construction and beyond this curing time, the stiffness increase was
slow. On the other hand, the fly ash+LKD and fly ash+lime test
sections indicated slower shorter-term increase in stiffness. The fly
ash+LKD and fly ash+lime section average resilient modulus values
at two years after construction were in excess of 800 ksi. Additional
longer-term testing data will be available from ongoing pavement
performance and environmental condition data collection at the two
pavement sites.
Abstract: Cancers could normally be marked by a number of
differentially expressed genes which show enormous potential as
biomarkers for a certain disease. Recent years, cancer classification
based on the investigation of gene expression profiles derived by
high-throughput microarrays has widely been used. The selection of
discriminative genes is, therefore, an essential preprocess step in
carcinogenesis studies. In this paper, we have proposed a novel gene
selector using information-theoretic measures for biological
discovery. This multivariate filter is a four-stage framework through
the analyses of feature relevance, feature interdependence, feature
redundancy-dependence and subset rankings, and having been
examined on the colon cancer data set. Our experimental result show
that the proposed method outperformed other information theorem
based filters in all aspect of classification errors and classification
performance.
Abstract: Current research has explored the impact of
instructional immediacy, defined as those behaviors that help build
close relationships or feelings of closeness, both on cognition and
motivation in the traditional classroom and online classroom;
however, online courses continue to suffer from higher dropout rates.
Based on Albert Bandura-s Social Cognitive Theory, four primary
relationships or interactions in an online course will be explored in
light of how they can provide immediacy thereby reducing student
attrition and improving cognitive learning. The four relationships are
teacher-student, student-student, and student-content, and studentcomputer.
Results of a study conducted with inservice teachers
completing a 14-week online professional development technology
course will be examined to demonstrate immediacy strategies that
improve cognitive learning and reduce student attrition. Results of
the study reveal that students can be motivated through various
interactions and instructional immediacy behaviors which lead to
higher completion rates, improved self-efficacy, and cognitive
learning.
Abstract: Pakistani doctors (MBBS) are emigrating towards developed countries for professional adjustments. This study aims to highlight causes and consequences of doctors- brain drain from Pakistan. Primary data was collected from Mayo Hospital, Lahore by interviewing doctors (n=100) through systematic random sampling technique. It found that various socio-economic and political conditions are working as push and pull factors for brain drain of doctors in Pakistan. Majority of doctors (83%) declared poor remunerations and professional infrastructure of health department as push factor of doctors- brain drain. 81% claimed that continuous instability in political situation and threats of terrorism are responsible for emigration of doctors. 84% respondents considered fewer opportunities of further studies responsible for their emigration. Brain drain of doctors is affecting health sector-s policies / programs, standard doctor-patient ratios and quality of health services badly.
Abstract: PPX(Pretty Printer for XML) is a query language that offers a concise description method of formatting the XML data into HTML. In this paper, we propose a simple specification of formatting method that is a combination description of automatic layout operators and variables in the layout expression of the GENERATE clause of PPX. This method can automatically format irregular XML data included in a part of XML with layout decision rule that is referred to DTD. In the experiment, a quick comparison shows that PPX requires far less description compared to XSLT or XQuery programs doing same tasks.
Abstract: Business process model describes process flow of a
business and can be seen as the requirement for developing a
software application. This paper discusses a BPM2CD guideline
which complements the Model Driven Architecture concept by
suggesting how to create a platform-independent software model in
the form of a UML class diagram from a business process model. An
important step is the identification of UML classes from the business
process model. A technique for object-oriented analysis called
domain analysis is borrowed and key concepts in the business
process model will be discovered and proposed as candidate classes
for the class diagram. The paper enhances this step by using ontology
search to help identify important classes for the business domain. As
ontology is a source of knowledge for a particular domain which
itself can link to ontologies of related domains, the search can give a
refined set of candidate classes for the resulting class diagram.
Abstract: This paper begins with formal defining of human rights and freedoms, and the basic document regarding the said subject is undoubtedly French Declaration of the Rights of Man and of the Citizen from 789. This paper furthermore parses legal sources relevant for the workers' rights in legal system of the Republic of Croatia, international contracts and the Labour Act, which is also a master bill regarding workers' rights The authors are also dealing with issues of Constitutional Court of the Republic of Croatia and its' position in judicial system of the Republic of Croatia, as well as with the specifics of Constitutional Complaint, and the crucial part of the paper is based on the research conducted with an aim to determine implementation of rights and liberties guaranteed by the articles 54. and 55. of the Constitution of the Republic of Croatia by means of Constitutional Complaint.
Abstract: Studies in neuroscience suggest that both global and
local feature information are crucial for perception and recognition of
faces. It is widely believed that local feature is less sensitive to
variations caused by illumination, expression and illumination. In
this paper, we target at designing and learning local features for face
recognition. We designed three types of local features. They are
semi-global feature, local patch feature and tangent shape feature.
The designing of semi-global feature aims at taking advantage of
global-like feature and meanwhile avoiding suppressing AdaBoost
algorithm in boosting weak classifies established from small local
patches. The designing of local patch feature targets at automatically
selecting discriminative features, and is thus different with traditional
ways, in which local patches are usually selected manually to cover
the salient facial components. Also, shape feature is considered in
this paper for frontal view face recognition. These features are
selected and combined under the framework of boosting algorithm
and cascade structure. The experimental results demonstrate that the
proposed approach outperforms the standard eigenface method and
Bayesian method. Moreover, the selected local features and
observations in the experiments are enlightening to researches in
local feature design in face recognition.
Abstract: Control chart pattern recognition is one of the most important tools to identify the process state in statistical process control. The abnormal process state could be classified by the recognition of unnatural patterns that arise from assignable causes. In this study, a wavelet based neural network approach is proposed for the recognition of control chart patterns that have various characteristics. The procedure of proposed control chart pattern recognizer comprises three stages. First, multi-resolution wavelet analysis is used to generate time-shape and time-frequency coefficients that have detail information about the patterns. Second, distance based features are extracted by a bi-directional Kohonen network to make reduced and robust information. Third, a back-propagation network classifier is trained by these features. The accuracy of the proposed method is shown by the performance evaluation with numerical results.
Abstract: This paper proposes a specialized Web robot to automatically collect objectionable Web contents for use in an objectionable Web content classification system, which creates the URL database of objectionable Web contents. It aims at shortening the update period of the DB, increasing the number of URLs in the DB, and enhancing the accuracy of the information in the DB.
Abstract: This paper introduces new algorithms (Fuzzy relative
of the CLARANS algorithm FCLARANS and Fuzzy c Medoids
based on randomized search FCMRANS) for fuzzy clustering of
relational data. Unlike existing fuzzy c-medoids algorithm (FCMdd)
in which the within cluster dissimilarity of each cluster is minimized
in each iteration by recomputing new medoids given current
memberships, FCLARANS minimizes the same objective function
minimized by FCMdd by changing current medoids in such away
that that the sum of the within cluster dissimilarities is minimized.
Computing new medoids may be effected by noise because outliers
may join the computation of medoids while the choice of medoids in
FCLARANS is dictated by the location of a predominant fraction of
points inside a cluster and, therefore, it is less sensitive to the
presence of outliers. In FCMRANS the step of computing new
medoids in FCMdd is modified to be based on randomized search.
Furthermore, a new initialization procedure is developed that add
randomness to the initialization procedure used with FCMdd. Both
FCLARANS and FCMRANS are compared with the robust and
linearized version of fuzzy c-medoids (RFCMdd). Experimental
results with different samples of the Reuter-21578, Newsgroups
(20NG) and generated datasets with noise show that FCLARANS is
more robust than both RFCMdd and FCMRANS. Finally, both
FCMRANS and FCLARANS are more efficient and their outputs
are almost the same as that of RFCMdd in terms of classification
rate.
Abstract: Dengue fever is an important human arboviral disease. Outbreaks are now reported quite often from many parts of the world. The number of cases involving pregnant women and infant cases are increasing every year. The illness is often severe and complications may occur. Deaths often occur because of the difficulties in early diagnosis and in the improper management of the diseases. Dengue antibodies from pregnant women are passed on to infants and this protects the infants from dengue infections. Antibodies from the mother are transferred to the fetus when it is still in the womb. In this study, we formulate a mathematical model to describe the transmission of this disease in pregnant women. The model is formulated by dividing the human population into pregnant women and non-pregnant human (men and non-pregnant women). Each class is subdivided into susceptible (S), infectious (I) and recovered (R) subclasses. We apply standard dynamical analysis to our model. Conditions for the local stability of the equilibrium points are given. The numerical simulations are shown. The bifurcation diagrams of our model are discussed. The control of this disease in pregnant women is discussed in terms of the threshold conditions.
Abstract: Versatile dual-mode class-AB CMOS four-quadrant
analog multiplier circuit is presented. The dual translinear loops and
current mirrors are the basic building blocks in realization scheme.
This technique provides; wide dynamic range, wide-bandwidth response
and low power consumption. The major advantages of this
approach are; its has single ended inputs; since its input is dual translinear
loop operate in class-AB mode which make this multiplier
configuration interesting for low-power applications; current multiplying,
voltage multiplying, or current and voltage multiplying can
be obtainable with balanced input. The simulation results of versatile
analog multiplier demonstrate a linearity error of 1.2 %, a -3dB bandwidth
of about 19MHz, a maximum power consumption of 0.46mW,
and temperature compensated. Operation of versatile analog multiplier
was also confirmed through an experiment using CMOS transistor
array.
Abstract: In this work, we improve a previously developed
segmentation scheme aimed at extracting edge information from
speckled images using a maximum likelihood edge detector. The
scheme was based on finding a threshold for the probability density
function of a new kernel defined as the arithmetic mean-to-geometric
mean ratio field over a circular neighborhood set and, in a general
context, is founded on a likelihood random field model (LRFM). The
segmentation algorithm was applied to discriminated speckle areas
obtained using simple elliptic discriminant functions based on
measures of the signal-to-noise ratio with fractional order moments.
A rigorous stochastic analysis was used to derive an exact expression
for the cumulative density function of the probability density
function of the random field. Based on this, an accurate probability
of error was derived and the performance of the scheme was
analysed. The improved segmentation scheme performed well for
both simulated and real images and showed superior results to those
previously obtained using the original LRFM scheme and standard
edge detection methods. In particular, the false alarm probability was
markedly lower than that of the original LRFM method with
oversegmentation artifacts virtually eliminated. The importance of
this work lies in the development of a stochastic-based segmentation,
allowing an accurate quantification of the probability of false
detection. Non visual quantification and misclassification in medical
ultrasound speckled images is relatively new and is of interest to
clinicians.
Abstract: The coalescer process is one of the methods for oily water treatment by increasing the oil droplet size in order to enhance the separating velocity and thus effective separation. However, the presence of surfactants in an oily emulsion can limit the obtained mechanisms due to the small oil size related with stabilized emulsion. In this regard, the purpose of this research is to improve the efficiency of the coalescer process for treating the stabilized emulsion. The effects of bed types, bed height, liquid flow rate and stage coalescer (step-bed) on the treatment efficiencies in term of COD values were studied. Note that the treatment efficiency obtained experimentally was estimated by using the COD values and oil droplet size distribution. The study has shown that the plastic media has more effective to attach with oil particles than the stainless one due to their hydrophobic properties. Furthermore, the suitable bed height (3.5 cm) and step bed (3.5 cm with 2 steps) were necessary in order to well obtain the coalescer performance. The application of step bed coalescer process in reactor has provided the higher treatment efficiencies in term of COD removal than those obtained with classical process. The proposed model for predicting the area under curve and thus treatment efficiency, based on the single collector efficiency (ηT) and the attachment efficiency (α), provides relatively a good coincidence between the experimental and predicted values of treatment efficiencies in this study.
Abstract: In this article, a method has been offered to classify
normal and defective tiles using wavelet transform and artificial
neural networks. The proposed algorithm calculates max and min
medians as well as the standard deviation and average of detail
images obtained from wavelet filters, then comes by feature vectors
and attempts to classify the given tile using a Perceptron neural
network with a single hidden layer. In this study along with the
proposal of using median of optimum points as the basic feature and
its comparison with the rest of the statistical features in the wavelet
field, the relational advantages of Haar wavelet is investigated. This
method has been experimented on a number of various tile designs
and in average, it has been valid for over 90% of the cases. Amongst
the other advantages, high speed and low calculating load are
prominent.
Abstract: This paper describes simple implementation of
homotopy (also called continuation) algorithm for determining the proper resistance of the resistor to dissipate energy at a specified rate of an electric circuit. Homotopy algorithm can be considered as a developing of the classical methods in numerical computing such as Newton-Raphson and fixed
point methods. In homoptopy methods, an embedding
parameter is used to control the convergence. The method purposed in this work utilizes a special homotopy called Newton homotopy. Numerical example solved in MATLAB is given to show the effectiveness of the purposed method