Abstract: Based on assumptions of neo-classical economics and
rational choice / public choice theory, this paper investigates the
regulation of industrial land use in Taiwan by homeowners
associations (HOAs) as opposed to traditional government
administration. The comparison, which applies the transaction cost
theory and a polynomial regression analysis, manifested that HOAs
are superior to conventional government administration in terms of
transaction costs and overall efficiency. A case study that compares
Taiwan-s commonhold industrial park, NangKang Software Park, to
traditional government counterparts using limited data on the costs
and returns was analyzed. This empirical study on the relative
efficiency of governmental and private institutions justified the
important theoretical proposition. Numerical results prove the
efficiency of the established model.
Abstract: The ElectroEncephaloGram (EEG) is useful for
clinical diagnosis and biomedical research. EEG signals often
contain strong ElectroOculoGram (EOG) artifacts produced
by eye movements and eye blinks especially in EEG recorded
from frontal channels. These artifacts obscure the underlying
brain activity, making its visual or automated inspection
difficult. The goal of ocular artifact removal is to remove
ocular artifacts from the recorded EEG, leaving the underlying
background signals due to brain activity. In recent times,
Independent Component Analysis (ICA) algorithms have
demonstrated superior potential in obtaining the least
dependent source components. In this paper, the independent
components are obtained by using the JADE algorithm (best
separating algorithm) and are classified into either artifact
component or neural component. Neural Network is used for
the classification of the obtained independent components.
Neural Network requires input features that exactly represent
the true character of the input signals so that the neural
network could classify the signals based on those key
characters that differentiate between various signals. In this
work, Auto Regressive (AR) coefficients are used as the input
features for classification. Two neural network approaches
are used to learn classification rules from EEG data. First, a
Polynomial Neural Network (PNN) trained by GMDH (Group
Method of Data Handling) algorithm is used and secondly,
feed-forward neural network classifier trained by a standard
back-propagation algorithm is used for classification and the
results show that JADE-FNN performs better than JADEPNN.
Abstract: One of the main advantages of the LO paradigm is to
allow the availability of good quality, shareable learning material
through the Web. The effectiveness of the retrieval process requires a
formal description of the resources (metadata) that closely fits the
user-s search criteria; in spite of the huge international efforts in this
field, educational metadata schemata often fail to fulfil this
requirement. This work aims to improve the situation, by the
definition of a metadata model capturing specific didactic features of
shareable learning resources. It classifies LOs into “teacher-oriented"
and “student-oriented" categories, in order to describe the role a LO
is to play when it is integrated into the educational process. This
article describes the model and a first experimental validation process
that has been carried out in a controlled environment.
Abstract: The problem of robust stability and robust stabilization for a class of discrete-time uncertain systems with time delay is investigated. Based on Tchebychev inequality, by constructing a new augmented Lyapunov function, some improved sufficient conditions ensuring exponential stability and stabilization are established. These conditions are expressed in the forms of linear matrix inequalities (LMIs), whose feasibility can be easily checked by using Matlab LMI Toolbox. Compared with some previous results derived in the literature, the new obtained criteria have less conservatism. Two numerical examples are provided to demonstrate the improvement and effectiveness of the proposed method.
Abstract: This paper explores the effectiveness of machine
learning techniques in detecting firms that issue fraudulent financial
statements (FFS) and deals with the identification of factors
associated to FFS. To this end, a number of experiments have been
conducted using representative learning algorithms, which were
trained using a data set of 164 fraud and non-fraud Greek firms in the
recent period 2001-2002. The decision of which particular method to
choose is a complicated problem. A good alternative to choosing
only one method is to create a hybrid forecasting system
incorporating a number of possible solution methods as components
(an ensemble of classifiers). For this purpose, we have implemented
a hybrid decision support system that combines the representative
algorithms using a stacking variant methodology and achieves better
performance than any examined simple and ensemble method. To
sum up, this study indicates that the investigation of financial
information can be used in the identification of FFS and underline the
importance of financial ratios.
Abstract: Recent years have seen a growing trend towards the
integration of multiple information sources to support large-scale
prediction of protein-protein interaction (PPI) networks in model
organisms. Despite advances in computational approaches, the
combination of multiple “omic" datasets representing the same type
of data, e.g. different gene expression datasets, has not been
rigorously studied. Furthermore, there is a need to further investigate
the inference capability of powerful approaches, such as fullyconnected
Bayesian networks, in the context of the prediction of PPI
networks. This paper addresses these limitations by proposing a
Bayesian approach to integrate multiple datasets, some of which
encode the same type of “omic" data to support the identification of
PPI networks. The case study reported involved the combination of
three gene expression datasets relevant to human heart failure (HF).
In comparison with two traditional methods, Naive Bayesian and
maximum likelihood ratio approaches, the proposed technique can
accurately identify known PPI and can be applied to infer potentially
novel interactions.
Abstract: There are three distinct stages in the evolution of
economic thought, namely:
1. in the first stage, the major concern was to accelerate
economic growth with increased availability of material
goods, especially in developing economies with very low
living standards, because poverty eradication meant faster
economic growth.
2. in the second stage, economists made distinction between
growth and development. Development was seen as going
beyond economic growth, and bringing certain changes in
the structure of the economy with more equitable
distribution of the benefits of growth, with the growth
coming automatic and sustained.
3. the third stage is now reached. Our concern is now with
“sustainable development", that is, development not only
for the present but also of the future.
Thus the focus changed from “sustained growth" to “sustained
development". Sustained development brings to the fore the long
term relationship between the ecology and economic development.
Since the creation of UNEP in 1972 it has worked for
development without destruction for environmentally sound and
sustained development. It was realised that the environment cannot
be viewed in a vaccum, it is not separate from development, nor is it
competing. It suggested for the integration of the environment with
development whereby ecological factors enter development planning,
socio-economic policies, cost-benefit analysis, trade, technology
transfer, waste management, educational and other specific areas.
Industrialisation has contributed to the growth of economy of
several countries. It has improved the standards of living of its people
and provided benefits to the society. It has also created in the process
great environmental problems like climate change, forest destruction
and denudation, soil erosion and desertification etc.
On the other hand, industry has provided jobs and improved the
prospects of wealth for the industrialists. The working class
communities had to simply put up with the high levels of pollution in
order to keep up their jobs and also to save their income.
There are many roots of the environmental problem. They may be
political, economic, cultural and technological conditions of the
modern society. The experts concede that industrial growth lies
somewhere close to the heart of the matter. Therefore, the objective
of this paper is not to document all roots of an environmental crisis
but rather to discuss the effects of industrial growth and
development.
We have come to the conclusion that although public intervention
is often unnecessary to ensure that perfectly competitive markets will
function in society-s best interests, such intervention is necessary
when firms or consumers pollute.
Abstract: While many studies have conducted the achievement
gap between groups of students in school districts, few studies have
utilized resilience research to investigate achievement gaps within
classrooms. This paper aims to summarize and discuss some recent
studies Waxman, Padr├│n, and their colleagues conducted, in which
they examined learning environment differences between resilient
and nonresilient students in reading and mathematics classrooms.
The classes consist of predominantly Hispanic elementary school
students from low-income families. These studies all incorporated
learning environment questionnaires and systematic observation
methods. Significant differences were found between resilient and
nonresilient students on their classroom learning environments and
classroom behaviors. The observation results indicate that the amount
and quality of teacher and student academic interaction are two of the
most influential variables that promote student outcomes. This paper
concludes by suggesting the following teacher practices to promote
resiliency in schools: (a) using feedback from classroom observation
and learning environment measures, (b) employing explicit teaching
practices; and (c) understanding students on a social and personal
level.
Abstract: The one-class support vector machine “support vector
data description” (SVDD) is an ideal approach for anomaly or outlier
detection. However, for the applicability of SVDD in real-world
applications, the ease of use is crucial. The results of SVDD are
massively determined by the choice of the regularisation parameter C
and the kernel parameter of the widely used RBF kernel. While for
two-class SVMs the parameters can be tuned using cross-validation
based on the confusion matrix, for a one-class SVM this is not
possible, because only true positives and false negatives can occur
during training. This paper proposes an approach to find the optimal
set of parameters for SVDD solely based on a training set from
one class and without any user parameterisation. Results on artificial
and real data sets are presented, underpinning the usefulness of the
approach.
Abstract: Much research into handwritten Thai character
recognition have been proposed, such as comparing heads of
characters, Fuzzy logic and structure trees, etc. This paper presents a
system of handwritten Thai character recognition, which is based on
the Ant-minor algorithm (data mining based on Ant colony
optimization). Zoning is initially used to determine each character.
Then three distinct features (also called attributes) of each character
in each zone are extracted. The attributes are Head zone, End point,
and Feature code. All attributes are used for construct the
classification rules by an Ant-miner algorithm in order to classify
112 Thai characters. For this experiment, the Ant-miner algorithm is
adapted, with a small change to increase the recognition rate. The
result of this experiment is a 97% recognition rate of the training set
(11200 characters) and 82.7% recognition rate of unseen data test
(22400 characters).
Abstract: The performance of adaptive beamforming degrades
substantially in the presence of steering vector mismatches. This
degradation is especially severe in the near-field, for the
3-dimensional source location is more difficult to estimate than the
2-dimensional direction of arrival in far-field cases. As a solution, a
novel approach of near-field robust adaptive beamforming (RABF) is
proposed in this paper. It is a natural extension of the traditional
far-field RABF and belongs to the class of diagonal loading
approaches, with the loading level determined based on worst-case
performance optimization. However, different from the methods
solving the optimal loading by iteration, it suggests here a simple
closed-form solution after some approximations, and consequently,
the optimal weight vector can be expressed in a closed form. Besides
simplicity and low computational cost, the proposed approach reveals
how different factors affect the optimal loading as well as the weight
vector. Its excellent performance in the near-field is confirmed via a
number of numerical examples.
Abstract: this paper presents a novel neural network controller
with composite adaptation low to improve the trajectory tracking
problems of biped robots comparing with classical controller. The
biped model has 5_link and 6 degrees of freedom and actuated by
Plated Pneumatic Artificial Muscle, which have a very high power to
weight ratio and it has large stoke compared to similar actuators. The
proposed controller employ a stable neural network in to approximate
unknown nonlinear functions in the robot dynamics, thereby
overcoming some limitation of conventional controllers such as PD
or adaptive controllers and guarantee good performance. This NN
controller significantly improve the accuracy requirements by
retraining the basic PD/PID loop, but adding an inner adaptive loop
that allows the controller to learn unknown parameters such as
friction coefficient, therefore improving tracking accuracy.
Simulation results plus graphical simulation in virtual reality show
that NN controller tracking performance is considerably better than
PD controller tracking performance.
Abstract: This paper studies the mean square exponential synchronization problem of a class of stochastic neutral type chaotic neural networks with mixed delay. On the Basis of Lyapunov stability theory, some sufficient conditions ensuring the mean square exponential synchronization of two identical chaotic neural networks are obtained by using stochastic analysis and inequality technique. These conditions are expressed in the form of linear matrix inequalities (LMIs), whose feasibility can be easily checked by using Matlab LMI Toolbox. The feedback controller used in this paper is more general than those used in previous literatures. One simulation example is presented to demonstrate the effectiveness of the derived results.
Abstract: In the automotive industry test drives are being conducted
during the development of new vehicle models or as a part of
quality assurance of series-production vehicles. The communication
on the in-vehicle network, data from external sensors, or internal
data from the electronic control units is recorded by automotive
data loggers during the test drives. The recordings are used for fault
analysis. Since the resulting data volume is tremendous, manually
analysing each recording in great detail is not feasible.
This paper proposes to use machine learning to support domainexperts
by preventing them from contemplating irrelevant data and
rather pointing them to the relevant parts in the recordings. The
underlying idea is to learn the normal behaviour from available
recordings, i.e. a training set, and then to autonomously detect
unexpected deviations and report them as anomalies.
The one-class support vector machine “support vector data description”
is utilised to calculate distances of feature vectors. SVDDSUBSEQ
is proposed as a novel approach, allowing to classify subsequences
in multivariate time series data. The approach allows to
detect unexpected faults without modelling effort as is shown with
experimental results on recordings from test drives.
Abstract: Knowledge sharing in general and the contextual
access to knowledge in particular, still represent a key challenge in
the knowledge management framework. Researchers on semantic
web and human machine interface study techniques to enhance this
access. For instance, in semantic web, the information retrieval is
based on domain ontology. In human machine interface, keeping
track of user's activity provides some elements of the context that can
guide the access to information. We suggest an approach based on
these two key guidelines, whilst avoiding some of their weaknesses.
The approach permits a representation of both the context and the
design rationale of a project for an efficient access to knowledge. In
fact, the method consists of an information retrieval environment
that, in the one hand, can infer knowledge, modeled as a semantic
network, and on the other hand, is based on the context and the
objectives of a specific activity (the design). The environment we
defined can also be used to gather similar project elements in order to
build classifications of tasks, problems, arguments, etc. produced in a
company. These classifications can show the evolution of design
strategies in the company.
Abstract: This Classifying Bird Sounds (chip notes) project-s
purpose is to reduce the unwanted noise from recorded bird sound
chip notes, design a scheme to detect differences and similarities
between recorded chip notes, and classify bird sound chip notes. The
technologies of determining the similarities of sound waves have
been used in communication, sound engineering and wireless sound
applications for many years. Our research is focused on the similarity
of chip notes, which are the sounds from different birds. The program
we use is generated by Microsoft Cµ.
Abstract: Results of Chilean wine classification based on the
information provided by an electronic nose are reported in this paper.
The classification scheme consists of two parts; in the first stage,
Principal Component Analysis is used as feature extraction method to
reduce the dimensionality of the original information. Then, Radial
Basis Functions Neural Networks is used as pattern recognition
technique to perform the classification. The objective of this study is
to classify different Cabernet Sauvignon, Merlot and Carménère wine
samples from different years, valleys and vineyards of Chile.
Abstract: This questionnaire-based study, aimed to measure and
compare the awareness of English reading strategies among EFL
learners at Bangkok University (BU) classified by their gender, field
of study, and English learning experience. Proportional stratified
random sampling was employed to formulate a sample of 380 BU
students. The data were statistically analyzed in terms of the mean
and standard deviation. t-Test analysis was used to find differences in
awareness of reading strategies between two groups (-male and
female- /-science and social-science students). In addition, one-way
analysis of variance (ANOVA) was used to compare reading strategy
awareness among BU students with different lengths of English
learning experience. The results of this study indicated that the
overall awareness of reading strategies of EFL learners at BU was at
a high level (ðÑ = 3.60) and that there was no statistically significant
difference between males and females, and among students who have
different lengths of English learning experience at the significance
level of 0.05. However, significant differences among students
coming from different fields of study were found at the same level of
significance.
Abstract: This paper presents an intrusion detection system of hybrid neural network model based on RBF and Elman. It is used for anomaly detection and misuse detection. This model has the memory function .It can detect discrete and related aggressive behavior effectively. RBF network is a real-time pattern classifier, and Elman network achieves the memory ability for former event. Based on the hybrid model intrusion detection system uses DARPA data set to do test evaluation. It uses ROC curve to display the test result intuitively. After the experiment it proves this hybrid model intrusion detection system can effectively improve the detection rate, and reduce the rate of false alarm and fail.
Abstract: The biomarker for colorectal cancer (CRC) is CEACAM-6 antigen (C6AG). Therefore, this study aims to develop a novel, simple and low-cost CEACAM-6 antigen immumosensor (C6AG-IMS), based on electrical impedance measurement, for precise determination of C6AG. A low-cost screen-printed graphite electrode was constructed and used as the sensor, with CEACAM-6 antibody (C6AB) immobilized on it. The procedures of sensor fabrication and antibody immobilization are simple and low-cost. Measurement of the electrical impedance at a definite frequency ranges (0.43 – 1.26 MHz) showed that the C6AG-IMS has an excellent linear (r2>0.9) response range (8.125 – 65 pg/mL), covering the normal physiological and pathological ranges of blood C6AG levels. Also, the C6AG-IMS has excellent reliability and validity, with the intraclass correlation coefficient being 0.97. In conclusion, a novel, simple, low-cost and reliable C6AG-IMS was designed and developed, being able to accurately determine blood C6AG levels in the range of pathological and normal physiological regions. The C6AG-IMS can provide a point-of-care and immediate screening results to the user at home.