Abstract: Web-based Cognitive Writing Instruction (WeCWI)’s
contribution towards language development can be divided into
linguistic and non-linguistic perspectives. In linguistic perspective,
WeCWI focuses on the literacy and language discoveries, while the
cognitive and psychological discoveries are the hubs in non-linguistic
perspective. In linguistic perspective, WeCWI draws attention to free
reading and enterprises, which are supported by the language
acquisition theories. Besides, the adoption of process genre approach
as a hybrid guided writing approach fosters literacy development.
Literacy and language developments are interconnected in the
communication process; hence, WeCWI encourages meaningful
discussion based on the interactionist theory that involves input,
negotiation, output, and interactional feedback. Rooted in the elearning
interaction-based model, WeCWI promotes online
discussion via synchronous and asynchronous communications,
which allows interactions happened among the learners, instructor,
and digital content. In non-linguistic perspective, WeCWI highlights
on the contribution of reading, discussion, and writing towards
cognitive development. Based on the inquiry models, learners’
critical thinking is fostered during information exploration process
through interaction and questioning. Lastly, to lower writing anxiety,
WeCWI develops the instructional tool with supportive features to
facilitate the writing process. To bring a positive user experience to
the learner, WeCWI aims to create the instructional tool with
different interface designs based on two different types of perceptual
learning style.
Abstract: This paper presents a combination of both robust
nonlinear controller and nonlinear controller for a class of nonlinear
4Y Octorotor UAV using Back-stepping and sliding mode controller.
The robustness against internal and external disturbance and
decoupling control are the merits of the proposed paper. The
proposed controller decouples the Octorotor dynamical system. The
controller is then applied to a 4Y Octortor UAV and its feature will
be shown.
Abstract: Passing the entrance exam to a university is a major
step in one's life. University entrance exam commonly known as
Kankor is the nationwide entrance exam in Afghanistan. This
examination is prerequisite for all public and private higher education
institutions at undergraduate level. It is usually taken by students who
are graduated from high schools. In this paper, we reflect the major
educational school graduates issues and propose ICT-based test
preparation environment, known as ‘Online Kankor Exam Prep
System’ to give students the tools to help them pass the university
entrance exam on the first try. The system is based on Intelligent
Tutoring System (ITS), which introduced an essential package of
educational technology for learners that features: (I) exam-focused
questions and content; (ii) self-assessment environment; and (iii) test
preparation strategies in order to help students to acquire the necessary
skills in their carrier and keep them up-to-date with instruction.
Abstract: We have developed a new computer program in
Fortran 90, in order to obtain numerical solutions of a system
of Relativistic Magnetohydrodynamics partial differential equations
with predetermined gravitation (GRMHD), capable of simulating
the formation of relativistic jets from the accretion disk of matter
up to his ejection. Initially we carried out a study on numerical
methods of unidimensional Finite Volume, namely Lax-Friedrichs,
Lax-Wendroff, Nessyahu-Tadmor method and Godunov methods
dependent on Riemann problems, applied to equations Euler in
order to verify their main features and make comparisons among
those methods. It was then implemented the method of Finite
Volume Centered of Nessyahu-Tadmor, a numerical schemes that
has a formulation free and without dimensional separation of
Riemann problem solvers, even in two or more spatial dimensions,
at this point, already applied in equations GRMHD. Finally, the
Nessyahu-Tadmor method was possible to obtain stable numerical
solutions - without spurious oscillations or excessive dissipation -
from the magnetized accretion disk process in rotation with respect
to a central black hole (BH) Schwarzschild and immersed in a
magnetosphere, for the ejection of matter in the form of jet over a
distance of fourteen times the radius of the BH, a record in terms
of astrophysical simulation of this kind. Also in our simulations,
we managed to get substructures jets. A great advantage obtained
was that, with the our code, we got simulate GRMHD equations in
a simple personal computer.
Abstract: Factors affecting construction unit cost vary
depending on a country’s political, economic, social and
technological inclinations. Factors affecting construction costs have
been studied from various perspectives. Analysis of cost factors
requires an appreciation of a country’s practices. Identified cost
factors provide an indication of a country’s construction economic
strata. The purpose of this paper is to identify the essential factors
that affect unit cost estimation and their breakdown using artificial
neural networks. Twenty five (25) identified cost factors in road
construction were subjected to a questionnaire survey and employing
SPSS factor analysis the factors were reduced to eight. The 8 factors
were analysed using neural network (NN) to determine the
proportionate breakdown of the cost factors in a given construction
unit rate. NN predicted that political environment accounted 44% of
the unit rate followed by contractor capacity at 22% and financial
delays, project feasibility and overhead & profit each at 11%. Project
location, material availability and corruption perception index had
minimal impact on the unit cost from the training data provided.
Quantified cost factors can be incorporated in unit cost estimation
models (UCEM) to produce more accurate estimates. This can create
improvements in the cost estimation of infrastructure projects and
establish a benchmark standard to assist the process of alignment of
work practises and training of new staff, permitting the on-going
development of best practises in cost estimation to become more
effective.
Abstract: There exists some time lag between the consumption of
inputs and the production of outputs. This time lag effect should be
considered in calculating efficiency of decision making units (DMU).
Recently, a couple of DEA models were developed for considering
time lag effect in efficiency evaluation of research activities. However,
these models can’t discriminate efficient DMUs because of the nature
of basic DEA model in which efficiency scores are limited to ‘1’. This
problem can be resolved a super-efficiency model. However, a super
efficiency model sometimes causes infeasibility problem. This paper
suggests an output oriented super-efficiency model for efficiency
evaluation under the consideration of time lag effect. A case example
using a long term research project is given to compare the suggested
model with the MpO model.
Abstract: Nowadays social media information, such as news,
links, images, or VDOs, is shared extensively. However, the
effectiveness of disseminating information through social media
lacks in quality: less fact checking, more biases, and several rumors.
Many researchers have investigated about credibility on Twitter, but
there is no the research report about credibility information on
Facebook. This paper proposes features for measuring credibility on
Facebook information. We developed the system for credibility on
Facebook. First, we have developed FB credibility evaluator for
measuring credibility of each post by manual human’s labelling. We
then collected the training data for creating a model using Support
Vector Machine (SVM). Secondly, we developed a chrome extension
of FB credibility for Facebook users to evaluate the credibility of
each post. Based on the usage analysis of our FB credibility chrome
extension, about 81% of users’ responses agree with suggested
credibility automatically computed by the proposed system.
Abstract: The problematic of gender and socioeconomic status
biased differences in academic motivation patterns is discussed.
Gender identity is understood according to symbolic interactionism
perspective: as a result of reflected appraisals, social comparisons,
self-attributions, and identifications, shaped by social environment
and family context. The effects of socioeconomic status on academic
motivation are conceptualized according to Bourdieu’s habitus
concept, reflecting the role of unconscious and internalized cultural
signals, proper to low and high socioeconomic status family contexts.
Since families differ by various socioeconomic features, the
hypothesis about possible impact of parents’ socioeconomic status on
their children’s academic motivation interfering with gender
socialization effects is held. The survey, aiming to seize gender
differences in academic motivation and self-recorded improvementoriented
efforts as a result of socialization processes operating in the
families of low and high socioeconomic status, was designed. The
results of Lithuanian higher education students’ survey are presented
and discussed.
Abstract: The exact theoretical expression describing the
probability distribution of nonlinear sea-surface elevations derived
from the second-order narrowband model has a cumbersome form
that requires numerical computations, not well-disposed to theoretical
or practical applications. Here, the same narrowband model is reexamined
to develop a simpler closed-form approximation suitable
for theoretical and practical applications. The salient features of the
approximate form are explored, and its relative validity is verified
with comparisons to other readily available approximations, and
oceanic data.
Abstract: In this paper, we present a robust algorithm to recognize extracted text from grocery product images captured by mobile phone cameras. Recognition of such text is challenging since text in grocery product images varies in its size, orientation,
style, illumination, and can suffer from perspective distortion.
Pre-processing is performed to make the characters scale and
rotation invariant. Since text degradations can not be appropriately
defined using well-known geometric transformations such
as translation, rotation, affine transformation and shearing, we
use the whole character black pixels as our feature vector.
Classification is performed with minimum distance classifier
using the maximum likelihood criterion, which delivers very
promising Character Recognition Rate (CRR) of 89%. We
achieve considerably higher Word Recognition Rate (WRR) of
99% when using lower level linguistic knowledge about product
words during the recognition process.
Abstract: The computational fluid dynamics (CFD) study of
stirred tank with the air-water interface are carried out in the presence
of different types of the impeller and with or without baffles. A
multiple reference frame (MRF) approach with the volume of fluid
(VOF) method is used to capture the air-water interface. The RANS
(Reynolds Averaged Navier-Stokes) equations with k-ε turbulence
model are solved to predict the flow behavior of water and air phase
which are treated as a different phases. The predicted results have
shown that the VOF method is able to capture the interface in the
unbaffled tank. While, the VOF method is showing an unfeasible
results in the baffled tank with high rotational impeller speed. For
continuous stirred tank, the air-water interface is disturbed by the
inflow and the level of water is also increased with time.
Abstract: Fuzzy inference method based approach to the
forming of modular intellectual system of assessment the quality of
communication services is proposed. Developed under this approach
the basic fuzzy estimation model takes into account the
recommendations of the International Telecommunication Union in
respect of the operation of packet switching networks based on IPprotocol.
To implement the main features and functions of the fuzzy
control system of quality telecommunication services it is used
multilayer feedforward neural network.
Abstract: Metal matrix composites (MMCs) attract considerable
attention as a result from its ability in providing a high strength, high
modulus, high toughness, high impact properties, improving wear
resistance and providing good corrosion resistance compared to
unreinforced alloy. Aluminium Silicon (Al/Si) alloy MMC has been
widely used in various industrial sectors such as in transportation,
domestic equipment, aerospace, military, construction, etc.
Aluminium silicon alloy is an MMC that had been reinforced with
aluminium nitrate (AlN) particle and become a new generation
material use in automotive and aerospace sector. The AlN is one of
the advance material that have a bright prospect in future since it has
features such as lightweight, high strength, high hardness and
stiffness quality. However, the high degree of ceramic particle
reinforcement and the irregular nature of the particles along the
matrix material that contribute to its low density is the main problem
which leads to difficulties in machining process. This paper examined
the tool wear when milling AlSi/AlN Metal Matrix Composite using
a TiB2 (Titanium diboride) coated carbide cutting tool. The volume
of the AlN reinforced particle was 10% and milling process was
carried out under dry cutting condition. The TiB2 coated carbide
insert parameters used were at the cutting speed of (230, 300 and
370m/min, feed rate of 0.8, Depth of Cut (DoC) at 0.4m). The
Sometech SV-35 video microscope system used to quantify of the
tool wear. The result shown that tool life span increasing with the
cutting speeds at (370m/min, feed rate of 0.8mm/tooth and DoC at
0.4mm) which constituted an optimum condition for longer tool life
lasted until 123.2 mins. Meanwhile, at medium cutting speed which
at 300m/m, feed rate of 0.8mm/tooth and depth of cut at 0.4mm we
found that tool life span lasted until 119.86 mins while at low cutting
speed it lasted in 119.66 mins. High cutting speed will give the best
parameter in cutting AlSi/AlN MMCs material. The result will help
manufacturers in machining process of AlSi/AlN MMCs materials.
Abstract: Red blood cells (RBC) are the most common types of
blood cells and are the most intensively studied in cell biology. The
lack of RBCs is a condition in which the amount of hemoglobin level
is lower than normal and is referred to as “anemia”. Abnormalities in
RBCs will affect the exchange of oxygen. This paper presents a
comparative study for various techniques for classifying the RBCs as
normal or abnormal (anemic) using WEKA. WEKA is an open
source consists of different machine learning algorithms for data
mining applications. The algorithms tested are Radial Basis Function
neural network, Support vector machine, and K-Nearest Neighbors
algorithm. Two sets of combined features were utilized for
classification of blood cells images. The first set, exclusively consist
of geometrical features, was used to identify whether the tested blood
cell has a spherical shape or non-spherical cells. While the second
set, consist mainly of textural features was used to recognize the
types of the spherical cells. We have provided an evaluation based on
applying these classification methods to our RBCs image dataset
which were obtained from Serdang Hospital - Malaysia, and
measuring the accuracy of test results. The best achieved
classification rates are 97%, 98%, and 79% for Support vector
machines, Radial Basis Function neural network, and K-Nearest
Neighbors algorithm respectively.
Abstract: Image spam is a kind of email spam where the spam
text is embedded with an image. It is a new spamming technique
being used by spammers to send their messages to bulk of internet
users. Spam email has become a big problem in the lives of internet
users, causing time consumption and economic losses. The main
objective of this paper is to detect the image spam by using histogram
properties of an image. Though there are many techniques to
automatically detect and avoid this problem, spammers employing
new tricks to bypass those techniques, as a result those techniques are
inefficient to detect the spam mails. In this paper we have proposed a
new method to detect the image spam. Here the image features are
extracted by using RGB histogram, HSV histogram and combination
of both RGB and HSV histogram. Based on the optimized image
feature set classification is done by using k- Nearest Neighbor(k-NN)
algorithm. Experimental result shows that our method has achieved
better accuracy. From the result it is known that combination of RGB
and HSV histogram with k-NN algorithm gives the best accuracy in
spam detection.
Abstract: In this paper, Fuzzy C-Means clustering with
Expectation Maximization-Gaussian Mixture Model based hybrid
modeling algorithm is proposed for Continuous Tamil Speech
Recognition. The speech sentences from various speakers are used
for training and testing phase and objective measures are between the
proposed and existing Continuous Speech Recognition algorithms.
From the simulated results, it is observed that the proposed algorithm
improves the recognition accuracy and F-measure up to 3% as
compared to that of the existing algorithms for the speech signal from
various speakers. In addition, it reduces the Word Error Rate, Error
Rate and Error up to 4% as compared to that of the existing
algorithms. In all aspects, the proposed hybrid modeling for Tamil
speech recognition provides the significant improvements for speechto-
text conversion in various applications.
Abstract: Ontologies offer a means for representing and sharing
information in many domains, particularly in complex domains. For
example, it can be used for representing and sharing information
of System Requirement Specification (SRS) of complex systems
like the SRS of ERTMS/ETCS written in natural language. Since
this system is a real-time and critical system, generic ontologies,
such as OWL and generic ERTMS ontologies provide minimal
support for modeling temporal information omnipresent in these SRS
documents. To support the modeling of temporal information, one
of the challenges is to enable representation of dynamic features
evolving in time within a generic ontology with a minimal redesign
of it. The separation of temporal information from other information
can help to predict system runtime operation and to properly design
and implement them. In addition, it is helpful to provide a reasoning
and querying techniques to reason and query temporal information
represented in the ontology in order to detect potential temporal
inconsistencies. To address this challenge, we propose a lightweight
3-layer temporal Quality of Service (QoS) ontology for representing,
reasoning and querying over temporal and non-temporal information
in a complex domain ontology. Representing QoS entities in separated
layers can clarify the distinction between the non QoS entities
and the QoS entities in an ontology. The upper generic layer of
the proposed ontology provides an intuitive knowledge of domain
components, specially ERTMS/ETCS components. The separation of
the intermediate QoS layer from the lower QoS layer allows us to
focus on specific QoS Characteristics, such as temporal or integrity
characteristics. In this paper, we focus on temporal information that
can be used to predict system runtime operation. To evaluate our
approach, an example of the proposed domain ontology for handover
operation, as well as a reasoning rule over temporal relations in this
domain-specific ontology, are presented.
Abstract: This paper presents the application of the Discrete
Component Model for heating and evaporation to multi-component
biodiesel fuel droplets in direct injection internal combustion engines.
This model takes into account the effects of temperature gradient,
recirculation and species diffusion inside droplets. A distinctive
feature of the model used in the analysis is that it is based on the
analytical solutions to the temperature and species diffusion
equations inside the droplets. Nineteen types of biodiesel fuels are
considered. It is shown that a simplistic model, based on the
approximation of biodiesel fuel by a single component or ignoring
the diffusion of components of biodiesel fuel, leads to noticeable
errors in predicted droplet evaporation time and time evolution of
droplet surface temperature and radius.
Abstract: Ontologies provide a common understanding of a
specific domain of interest that can be communicated between people
and used as background knowledge for automated reasoning in a
wide range of applications. In this paper, we address the design of
multilingual ontologies following well-defined knowledge
engineering methodologies with the support of novel collaborative
development approaches. In particular, we present a collaborative
platform which allows ontologies to be developed incrementally in
multiple languages. This is made possible via an appropriate mapping
between language independent concepts and one lexicalization per
language (or a lexical gap in case such lexicalization does not exist).
The collaborative platform has been designed to support the
development of the Universal Knowledge Core, a multilingual
ontology currently in English, Italian, Chinese, Mongolian, Hindi and
Bangladeshi. Its design follows a workflow-based development
methodology that models resources as a set of collaborative objects
and assigns customizable workflows to build and maintain each
collaborative object in a community driven manner, with extensive
support of modern web 2.0 social and collaborative features.
Abstract: The financial crises caused a collapse in prices of
most asset classes, raising the attention on alternative investments
such as sukuk, a smaller, fast growing but often misunderstood
market. We study diversification benefits of sukuk, their correlation
with other asset classes and the effects of their inclusion in
investment portfolios of institutional and retail investors, through a
comprehensive comparison of their risk/return profiles during and
after the financial crisis.
We find a beneficial performance adjusted for the specific
volatility together with a lower correlation especially during the
financial crisis. The distribution of sukuk returns is positively skewed
and leptokurtic, with a risk/return profile similarly to high yield
bonds. Overall, our results suggest that sukuk present diversification
opportunities, a significant volatility-adjusted performance and lower
correlations especially during the financial crisis.
Our findings are relevant for a number of institutional investors.
Long term investors, such as life insurers would benefit from sukuk’s
protective features during financial crisis yet keeping return and
growth opportunities, whereas banks would gain due to their role of
placers, advisors, market makers or underwriters.