Abstract: A sign pattern is a matrix whose entries belong to the set
{+,−, 0}. An n-by-n sign pattern A is said to allow an eventually
positive matrix if there exist some real matrices A with the same
sign pattern as A and a positive integer k0 such that Ak > 0 for all
k ≥ k0. It is well known that identifying and classifying the n-by-n
sign patterns that allow an eventually positive matrix are posed as two
open problems. In this article, the tree sign patterns of small order
that allow an eventually positive matrix are classified completely.
Abstract: Social networking sites such as Twitter and Facebook
attracts over 500 million users across the world, for those users, their
social life, even their practical life, has become interrelated. Their
interaction with social networking has affected their life forever.
Accordingly, social networking sites have become among the main
channels that are responsible for vast dissemination of different kinds
of information during real time events. This popularity in Social
networking has led to different problems including the possibility of
exposing incorrect information to their users through fake accounts
which results to the spread of malicious content during life events.
This situation can result to a huge damage in the real world to the
society in general including citizens, business entities, and others. In this paper, we present a classification method for detecting the
fake accounts on Twitter. The study determines the minimized set of
the main factors that influence the detection of the fake accounts on
Twitter, and then the determined factors are applied using different
classification techniques. A comparison of the results of these
techniques has been performed and the most accurate algorithm is
selected according to the accuracy of the results. The study has been
compared with different recent researches in the same area; this
comparison has proved the accuracy of the proposed study. We claim
that this study can be continuously applied on Twitter social network
to automatically detect the fake accounts; moreover, the study can be
applied on different social network sites such as Facebook with minor
changes according to the nature of the social network which are
discussed in this paper.
Abstract: In this paper, according to the classical algorithm
LSQR for solving the least-squares problem, an iterative method is
proposed for least-squares solution of constrained matrix equation. By
using the Kronecker product, the matrix-form LSQR is presented to
obtain the like-minimum norm and minimum norm solutions in a
constrained matrix set for the symmetric arrowhead matrices. Finally,
numerical examples are also given to investigate the performance.
Abstract: The study of implicature which is one of the
discussions of pragmatics is such an interesting and challenging topic
to discuss. Implicature is such a meaning which is implied in such an
utterance which is not the same as its literal meaning. The rapid
development of information technology results social networks as
media to broadcast messages. The broadcast messages may be in the
form of jokes which contain implicature. The research applies the
pragmatic equivalent method to analyze the topics of jokes based on
the implicatures contained in them. Furthermore, the method is also
applied to reveal the purpose of creating implicature in jokes. The
findings include the kinds of implicature found in jokes which are
classified into conventional implicature and conversational
implicature. Then, in detailed analysis, implicature in jokes is divided
into implicature related to gender, culture, and social phenomena.
Furthermore, implicature in jokes may not only be used to give
entertainment but also to soften criticisms or satire so that it does not
sound rude and harsh.
Abstract: The study is the way to identify the problems that
occur in organizing short course’s lifelong learning in the information
and communication technology (ICT) education which are faced by
the lecturer and staff at the Mara Skill Institute and Industrial
Training Institute in Pahang Malaysia. The important aspects of these
issues are classified to five which are selecting the courses
administrative. Fifty lecturers and staff were selected as a respondent.
The sample is selected by using the non-random sampling method
purpose sampling. The questionnaire is used as a research instrument
and divided into five main parts. All the data that gain from the
questionnaire are analyzed by using the SPSS in term of mean,
standard deviation and percentage. The findings showed, there are the
problems occur in organizing the short course for lifelong learning in
ICT education.
Abstract: The main purpose of this study is to assess the
sediment quality and potential ecological risk in marine sediments in
Gymea Bay located in south Sydney, Australia. A total of 32 surface
sediment samples were collected from the bay. Current track
trajectories and velocities have also been measured in the bay. The
resultant trace elements were compared with the adverse biological
effect values Effect Range Low (ERL) and Effect Range Median
(ERM) classifications. The results indicate that the average values of
chromium, arsenic, copper, zinc, and lead in surface sediments all
reveal low pollution levels and are below ERL and ERM values. The
highest concentrations of trace elements were found close to
discharge points and in the inner bay, and were linked with high
percentages of clay minerals, pyrite and organic matter, which can
play a significant role in trapping and accumulating these elements.
The lowest concentrations of trace elements were found to be on the
shoreline of the bay, which contained high percentages of sand
fractions. It is postulated that the fine particles and trace elements are
disturbed by currents and tides, then transported and deposited in
deeper areas. The current track velocities recorded in Gymea Bay had
the capability to transport fine particles and trace element pollution
within the bay. As a result, hydrodynamic measurements were able to
provide useful information and to help explain the distribution of
sedimentary particles and geochemical properties. This may lead to
knowledge transfer to other bay systems, including those in remote
areas. These activities can be conducted at a low cost, and are
therefore also transferrable to developing countries. The advent of
portable instruments to measure trace elements in the field has also
contributed to the development of these lower cost and easily applied
methodologies available for use in remote locations and low-cost
economies.
Abstract: Common Platform for Automated Programming
(CPAP) is defined in details. Two versions of CPAP are described:
Cloud based (including set of components for classic programming,
and set of components for combined programming); and Knowledge
Based Automated Software Engineering (KBASE) based (including
set of components for automated programming, and set of
components for ontology programming). Four KBASE products
(Module for Automated Programming of Robots, Intelligent Product
Manual, Intelligent Document Display, and Intelligent Form
Generator) are analyzed and CPAP contributions to automated
programming are presented.
Abstract: The aim of this paper is to propose a general
framework for storing, analyzing, and extracting knowledge from
two-dimensional echocardiographic images, color Doppler images,
non-medical images, and general data sets. A number of high
performance data mining algorithms have been used to carry out this
task. Our framework encompasses four layers namely physical
storage, object identification, knowledge discovery, user level.
Techniques such as active contour model to identify the cardiac
chambers, pixel classification to segment the color Doppler echo
image, universal model for image retrieval, Bayesian method for
classification, parallel algorithms for image segmentation, etc., were
employed. Using the feature vector database that have been
efficiently constructed, one can perform various data mining tasks
like clustering, classification, etc. with efficient algorithms along
with image mining given a query image. All these facilities are
included in the framework that is supported by state-of-the-art user
interface (UI). The algorithms were tested with actual patient data
and Coral image database and the results show that their performance
is better than the results reported already.
Abstract: The purposes of this research are to make comparisons in
respect of the behaviors on the use of the services of metered taxi
classified by the demographic factor and to study the influence of the
recognition on service quality having the effect on usage behaviors of
metered taxi services of consumers in Bangkok Metropolitan Areas. The
samples used in this research were 400 metered taxi service users in
Bangkok Metropolitan Areas and questionnaire was used as the tool for
collecting the data. Analysis statistics are mean and multiple regression
analysis. Results of the research revealed that the consumers recognize the
overall quality of services in each aspect include tangible aspects of the
service, responses to customers, assurance on the confidence,
understanding and knowing of customers which is rated at the moderate
level except the aspect of the assurance on the confidence and
trustworthiness which are rated at a high level. For the result of
hypothetical test, it is found that the quality in providing the services on
the aspect of the assurance given to the customers has the effect on the
usage behaviors of metered taxi services and the aspect of the frequency
on the use of the services per month which in this connection. Such
variable can forecast at one point nine percent (1.9%). In addition, quality
in providing the services and the aspect of the responses to customers
have the effect on the behaviors on the use of metered taxi services on the
aspect of the expenses on the use of services per month which in this
connection, such variable can forecast at two point one percent (2.1%).
Abstract: This study investigates how the site specific traffic
data differs from the Mechanistic Empirical Pavement Design
Software default values. Two Weigh-in-Motion (WIM) stations were
installed in Interstate-40 (I-40) and Interstate-25 (I-25) to developed
site specific data. A computer program named WIM Data Analysis
Software (WIMDAS) was developed using Microsoft C-Sharp (.Net)
for quality checking and processing of raw WIM data. A complete
year data from November 2013 to October 2014 was analyzed using
the developed WIM Data Analysis Program. After that, the vehicle
class distribution, directional distribution, lane distribution, monthly
adjustment factor, hourly distribution, axle load spectra, average
number of axle per vehicle, axle spacing, lateral wander distribution,
and wheelbase distribution were calculated. Then a comparative
study was done between measured data and AASHTOWare default
values. It was found that the measured general traffic inputs for I-40
and I-25 significantly differ from the default values.
Abstract: Sentiment analysis means to classify a given review
document into positive or negative polar document. Sentiment
analysis research has been increased tremendously in recent times
due to its large number of applications in the industry and academia.
Sentiment analysis models can be used to determine the opinion of
the user towards any entity or product. E-commerce companies can
use sentiment analysis model to improve their products on the basis
of users’ opinion. In this paper, we propose a new One-class Support
Vector Machine (One-class SVM) based sentiment analysis model
for movie review documents. In the proposed approach, we initially
extract features from one class of documents, and further test the
given documents with the one-class SVM model if a given new test
document lies in the model or it is an outlier. Experimental results
show the effectiveness of the proposed sentiment analysis model.
Abstract: We present probabilistic multinomial Dirichlet
classification model for multidimensional data and Gaussian process
priors. Here, we have considered efficient computational method that
can be used to obtain the approximate posteriors for latent variables
and parameters needed to define the multiclass Gaussian process
classification model. We first investigated the process of inducing a
posterior distribution for various parameters and latent function by
using the variational Bayesian approximations and important sampling
method, and next we derived a predictive distribution of latent
function needed to classify new samples. The proposed model is
applied to classify the synthetic multivariate dataset in order to verify
the performance of our model. Experiment result shows that our model
is more accurate than the other approximation methods.
Abstract: This paper introduces an effective method of
segmenting Korean text (place names in Korean) from a Korean road
sign image. A Korean advanced directional road sign is composed of
several types of visual information such as arrows, place names in
Korean and English, and route numbers. Automatic classification of
the visual information and extraction of Korean place names from the
road sign images make it possible to avoid a lot of manual inputs to a
database system for management of road signs nationwide. We
propose a series of problem-specific heuristics that correctly segments
Korean place names, which is the most crucial information, from the
other information by leaving out non-text information effectively. The
experimental results with a dataset of 368 road sign images show 96%
of the detection rate per Korean place name and 84% per road sign
image.
Abstract: A seizure prediction method is proposed by extracting
global features using phase correlation between adjacent epochs for
detecting relative changes and local features using fluctuation/
deviation within an epoch for determining fine changes of different
EEG signals. A classifier and a regularization technique are applied
for the reduction of false alarms and improvement of the overall
prediction accuracy. The experiments show that the proposed method
outperforms the state-of-the-art methods and provides high prediction
accuracy (i.e., 97.70%) with low false alarm using EEG signals in
different brain locations from a benchmark data set.
Abstract: In this paper, a robust fault detection and isolation
(FDI) scheme is developed to monitor a multivariable nonlinear
chemical process called the Chylla-Haase polymerization reactor,
when it is under the cascade PI control. The scheme employs a radial
basis function neural network (RBFNN) in an independent mode to
model the process dynamics, and using the weighted sum-squared
prediction error as the residual. The Recursive Orthogonal Least
Squares algorithm (ROLS) is employed to train the model to
overcome the training difficulty of the independent mode of the
network. Then, another RBFNN is used as a fault classifier to isolate
faults from different features involved in the residual vector. Several
actuator and sensor faults are simulated in a nonlinear simulation of
the reactor in Simulink. The scheme is used to detect and isolate the
faults on-line. The simulation results show the effectiveness of the
scheme even the process is subjected to disturbances and
uncertainties including significant changes in the monomer feed rate,
fouling factor, impurity factor, ambient temperature, and
measurement noise. The simulation results are presented to illustrate
the effectiveness and robustness of the proposed method.
Abstract: The modelling of physical phenomena, such as the
earth’s free oscillations, the vibration of strings, the interaction of
atomic particles, or the steady state flow in a bar give rise to Sturm-
Liouville (SL) eigenvalue problems. The boundary applications of
some systems like the convection-diffusion equation, electromagnetic
and heat transfer problems requires the combination of Dirichlet and
Neumann boundary conditions. Hence, the incorporation of Robin
boundary condition in the analyses of Sturm-Liouville problem. This
paper deals with the computation of the eigenvalues and
eigenfunction of generalized Sturm-Liouville problems with Robin
boundary condition using the finite element method. Numerical
solution of classical Sturm–Liouville problem is presented. The
results show an agreement with the exact solution. High results
precision is achieved with higher number of elements.
Abstract: Global economy today is full of sophistication. All
over the world, business and marketing practices are undergoing
unprecedented transformation. In realization of this fact, the federal
government of Nigeria has put in place a robust transformation
agenda in order to put Nigeria in a better position to be a competitive
player and in the process transform all sectors of its economy. New
technologies, especially the Internet, are the driving force behind this
transformation. However, technology has inadvertently affected the
way businesses are done thus necessitating the acquisition of new
skills. In developing countries like Nigeria, citizens are still battling
with effective application of those technologies. Obviously, students
of business education need to acquire relevant business knowledge to
be able to transit into the world of work on graduation from school
and compete favorably in the labor market. Therefore, effective
utilization of social media by both teachers and students can help
extensively in empowering students with the needed skills. Social
media which is a group of Internet-based applications built on the
ideological foundations of Web 2.0, that allow the creation and
exchange of user generated content, and if incorporated into the
classroom experience may be the needed answer to unemployment
and poverty in Nigeria as beneficiaries can easily connect with
existing and potential enterprises and customers, engage with them
and reinforce mutual business benefits. Challenges and benefits of
social media use in education in Nigeria universities were revealed in
this study.
Abstract: The underutilization of biomass resources in the
Philippines, combined with its growing population and the rise in
fossil fuel prices confirms demand for alternative energy sources. The
goal of this paper is to provide a comparison of MODIS-based and
Landsat-based agricultural land cover maps when used in the
estimation of rice hull’s available energy potential. Biomass resource
assessment was done using mathematical models and remote sensing
techniques employed in a GIS platform.
Abstract: The understanding of geotechnical characteristics of
near-surface material and the effects of the groundwater is very
important problem in such as site studies. For showing the relations
between seismic data and groundwater, we selected about 25 km2 as
the study area. It has been presented which is a detailed work of
seismic data and groundwater depths of Gokpinar Damp area.
Seismic waves velocity (Vp and Vs) are very important parameters
showing the soil properties. The seismic records were used the
method of the multichannel analysis of surface waves near area of
Gokpinar Damp area. Sixty sites in this area have been investigated
with survey lines about 60 m in length. MASW (Multichannel
analysis of surface wave) method has been used to generate onedimensional
shear wave velocity profile at locations. These shear
wave velocities are used to estimate equivalent shear wave velocity in
the study area at every 2 and 5 m intervals up to a depth of 45 m.
Levels of equivalent shear wave velocity of soil are used the
classified of the study area. After the results of the study, it must be
considered as components of urban planning and building design of
Gokpinar Damp area, Denizli and the application and use of these
results should be required and enforced by municipal authorities.
Abstract: In this paper, we propose the variational EM inference
algorithm for the multi-class Gaussian process classification model
that can be used in the field of human behavior recognition. This
algorithm can drive simultaneously both a posterior distribution of a
latent function and estimators of hyper-parameters in a Gaussian
process classification model with multiclass. Our algorithm is based
on the Laplace approximation (LA) technique and variational EM
framework. This is performed in two steps: called expectation and
maximization steps. First, in the expectation step, using the Bayesian
formula and LA technique, we derive approximately the posterior
distribution of the latent function indicating the possibility that each
observation belongs to a certain class in the Gaussian process
classification model. Second, in the maximization step, using a derived
posterior distribution of latent function, we compute the maximum
likelihood estimator for hyper-parameters of a covariance matrix
necessary to define prior distribution for latent function. These two
steps iteratively repeat until a convergence condition satisfies.
Moreover, we apply the proposed algorithm with human action
classification problem using a public database, namely, the KTH
human action data set. Experimental results reveal that the proposed
algorithm shows good performance on this data set.