Abstract: Crude oil blending is an important unit operation in
petroleum refining industry. A good model for the blending system is
beneficial for supervision operation, prediction of the export
petroleum quality and realizing model-based optimal control. Since
the blending cannot follow the ideal mixing rule in practice, we
propose a static neural network to approximate the blending
properties. By the dead-zone approach, we propose a new robust
learning algorithm and give theoretical analysis. Real data of crude
oil blending is applied to illustrate the neuro modeling approach.
Abstract: Ecological ponds can be a good teaching tool for
science teachers, but they must be built and maintained properly to
provide students with a safe and suitable learning environment.
Hence, many schools do not have the ability to build an ecological
pond. This study used virtual reality technology to develop a webbased
virtual ecological pond. Supported by situated learning theory
and the instructional design of “Aquatic Life" learning unit,
elementary school students can actively explore in the virtual
ecological pond to observe aquatic animals and plants and learn
about the concept of ecological conservation. A teaching experiment
was conducted to investigate the learning effectiveness and
practicability of this instructional design, and the results showed that
students improved a great deal in learning about aquatic life. They
found the virtual ecological pond interesting, easy to operate and
helpful to understanding the aquatic ecological system. Therefore, it
is useful in elementary science education.
Abstract: This paper aims to describe how student satisfaction is
measured for work-based learners as these are non-traditional
learners, conducting academic learning in the workplace, typically
their curricula have a high degree of negotiation, and whose
motivations are directly related to their employers- needs, as well as
their own career ambitions. We argue that while increasing WBL
participation, and use of SSD are both accepted as being of strategic
importance to the HE agenda, the use of WBL SSD is rarely
examined, and lessons can be learned from the comparison of SSD
from a range of WBL programmes, and increased visibility of this
type of data will provide insight into ways to improve and develop
this type of delivery. The key themes that emerged from the analysis
of the interview data were: learners profiles and needs, employers
drivers, academic staff drivers, organizational approach, tools for
collecting data and visibility of findings. The paper concludes with
observations on best practice in the collection, analysis and use of
WBL SSD, thus offering recommendations for both academic
managers and practitioners.
Abstract: As the network based technologies become
omnipresent, demands to secure networks/systems against threat
increase. One of the effective ways to achieve higher security is
through the use of intrusion detection systems (IDS), which are a
software tool to detect anomalous in the computer or network. In this
paper, an IDS has been developed using an improved machine
learning based algorithm, Locally Linear Neuro Fuzzy Model
(LLNF) for classification whereas this model is originally used for
system identification. A key technical challenge in IDS and LLNF
learning is the curse of high dimensionality. Therefore a feature
selection phase is proposed which is applicable to any IDS. While
investigating the use of three feature selection algorithms, in this
model, it is shown that adding feature selection phase reduces
computational complexity of our model. Feature selection algorithms
require the use of a feature goodness measure. The use of both a
linear and a non-linear measure - linear correlation coefficient and
mutual information- is investigated respectively
Abstract: Collaborative networked learning (hereafter CNL)
was first proposed by Charles Findley in his work “Collaborative
networked learning: online facilitation and software support" as part
of instructional learning for the future of the knowledge worker. His
premise was that through electronic dialogue learners and experts
could interactively communicate within a contextual framework to
resolve problems, and/or to improve product or process knowledge.
Collaborative learning has always been the forefront of educational
technology and pedagogical research, but not in the mainstream of
operations management. As a result, there is a large disparity in the
study of CNL, and little is known about the antecedents of network
collaboration and sharing of information among diverse employees in
the manufacturing environment. This paper presents a model to
bridge the gap between theory and practice. The objective is that
manufacturing organizations will be able to accelerate organizational
learning and sharing of information through various collaborative
Abstract: The assessment of the efficacy of devised Mobile-
Assisted Instructional Modes in Mobile Learning was the focus of
this research. The study adopted pre-test, post-test, control group
quasi-experimental design. Research instruments were developed,
validated and used for collecting data. Findings revealed that the
students exposed to Mobile Task Based Learning Mode (MTBLM) in
using Mobile-Assisted Instruction (MAI) performed significantly
better. The implication of these findings is that, the Audio tutorial
and Practice Mode (ATPM) (Stimulus instruments) of MAI had been
found better over the other modes used in the study.
Abstract: Software engineering education not only embraces
technical skills of software development but also necessitates
communication and interaction among learners. In this paper, it is
proposed to adapt the PBL methodology that is especially designed to
be integrated into software engineering classroom in order to promote
collaborative learning environment. This approach helps students
better understand the significance of social aspects and provides a
systematic framework to enhance teamwork skills. The adaptation of
PBL facilitates the transition to an innovative software development
environment where cooperative learning can be actualized.
Abstract: The burst of Web 2.0 technology and social
networking tools manifest different styles of learning and managing
knowledge among both knowledge workers and adult learners. In the
Western countries, open-learning concept has been made popular due
to the ease of use and the reach that the technology provides. In
Malaysia, there are still some gaps between the learners- acceptance
of technology and the full implementation of the technology in the
education system. There is a need to understand how adult learners,
who are knowledge workers, manage their personal knowledge via
social networking tools, especially in their learning process. Four
processes of personal knowledge management (PKM) and four
cognitive enablers are proposed supported by analysed data on adult
learners in a university. The model derived from these processes and
enablers is tested and presented, with recommendations on features to be included in adult learners- learning environment.
Abstract: Professional development is the focus of this study. It
reports on questionnaire data that examined the perceived
effectiveness of the Train the Trainer model of technology
professional development for elementary teachers. Eighty-three
selected teachers called Information Technology Coaches received
four half-day and one after-school in-service sessions. Subsequently,
coaches shared the information and skills acquired during training
with colleagues. Results indicated that participants felt comfortable
as Information Technology Coaches and felt well prepared because
of their technological professional development. Overall, participants
perceived the Train the Trainer model to be effective. The outcomes
of this study suggest that the use of the Train the Trainer model, a
known professional development model, can be an integral and
interdependent component of the newer more comprehensive
learning community professional development model.
Abstract: Logic based methods for learning from structured data
is limited w.r.t. handling large search spaces, preventing large-sized
substructures from being considered by the resulting classifiers. A
novel approach to learning from structured data is introduced that
employs a structure transformation method, called finger printing, for
addressing these limitations. The method, which generates features
corresponding to arbitrarily complex substructures, is implemented in
a system, called DIFFER. The method is demonstrated to perform
comparably to an existing state-of-art method on some benchmark
data sets without requiring restrictions on the search space.
Furthermore, learning from the union of features generated by finger
printing and the previous method outperforms learning from each
individual set of features on all benchmark data sets, demonstrating
the benefit of developing complementary, rather than competing,
methods for structure classification.
Abstract: In this work a visual and reactive contour following
behaviour is learned by reinforcement. With artificial vision the
environment is perceived in 3D, and it is possible to avoid obstacles
that are invisible to other sensors that are more common in mobile
robotics. Reinforcement learning reduces the need for intervention in
behaviour design, and simplifies its adjustment to the environment,
the robot and the task. In order to facilitate its generalisation to other
behaviours and to reduce the role of the designer, we propose a
regular image-based codification of states. Even though this is much
more difficult, our implementation converges and is robust. Results
are presented with a Pioneer 2 AT on a Gazebo 3D simulator.
Abstract: Since the presentation of the backpropagation algorithm, a vast variety of improvements of the technique for training a feed forward neural networks have been proposed. This article focuses on two classes of acceleration techniques, one is known as Local Adaptive Techniques that are based on weightspecific only, such as the temporal behavior of the partial derivative of the current weight. The other, known as Dynamic Adaptation Methods, which dynamically adapts the momentum factors, α, and learning rate, η, with respect to the iteration number or gradient. Some of most popular learning algorithms are described. These techniques have been implemented and tested on several problems and measured in terms of gradient and error function evaluation, and percentage of success. Numerical evidence shows that these techniques improve the convergence of the Backpropagation algorithm.
Abstract: The increasing recognition of the need for education to be closely aligned with team playing, project based learning and problem solving approaches has increase the interest in collaborative learning among university and college instructors. Using online collaboration learning in learning can enhance the outcome and achievement of students as well as improve their communication, critical thinking and personnel skills. The current research aims at examining the effect of OCL on the student's achievement at Kingdom of Bahrain. Numbers of objectives were set to achieve the aim of the research include: investigating the current situation regarding the collaborative learning and OCL at the Kingdom of Bahrain by identifying the advantages and effectiveness of OCL as a learning tool over traditional learning, examining the factors that affect OCL as well as examining the impact of OCL on the student's achievement. To achieve these objectives, quantitative method was adopted. Two hundred and thirty one questionnaires were distributed to students in different local and private universities at Kingdom of Bahrain. The findings of the research show that most of the students prefer to use FTFCL in learning and that OCL is already adopted in some universities especially in University of Bahrain. Moreover, the most factors affecting the adopted OCL are perceived readiness, and guidance and support.
Abstract: This communication is intended to provide some issues for thought on the importance of implementation of Blended Learning in traditional universities, particularly in the Spanish university system. In this respect, we believe that virtual environments are likely to meet some of the needs raised by the Bologna agreement, trying to maintain the quality of teaching and at the same time taking advantage of the functionalities that virtual learning platforms offer. We are aware that an approach of learning from an open and constructivist nature in universities is a complex process that faces significant technological, administrative and human barriers. Therefore, in order to put plans in our universities, it is necessary to analyze the state of the art of some indicators relating to the use of ICT, with special attention to virtual teaching and learning, so that we can identify the main obstacles and design adaptive strategies for their full integration in the education system. Finally, we present major initiatives launched in the European and state framework for the effective implementation of new virtual environments in the area of higher education.
Abstract: To create a solution for a specific problem in machine
learning, the solution is constructed from the data or by use a search
method. Genetic algorithms are a model of machine learning that can
be used to find nearest optimal solution. While the great advantage of
genetic algorithms is the fact that they find a solution through
evolution, this is also the biggest disadvantage. Evolution is inductive,
in nature life does not evolve towards a good solution but it evolves
away from bad circumstances. This can cause a species to evolve into
an evolutionary dead end. In order to reduce the effect of this
disadvantage we propose a new a learning tool (criteria) which can be
included into the genetic algorithms generations to compare the
previous population and the current population and then decide
whether is effective to continue with the previous population or the
current population, the proposed learning tool is called as Keeping
Efficient Population (KEP). We applied a GA based on KEP to the
production line layout problem, as a result KEP keep the evaluation
direction increases and stops any deviation in the evaluation.
Abstract: We present an Electronic Nose (ENose), which is
aimed at identifying the presence of one out of two gases, possibly
detecting the presence of a mixture of the two. Estimation of the
concentrations of the components is also performed for a volatile
organic compound (VOC) constituted by methanol and acetone, for
the ranges 40-400 and 22-220 ppm (parts-per-million), respectively.
Our system contains 8 sensors, 5 of them being gas sensors (of the
class TGS from FIGARO USA, INC., whose sensing element is a tin
dioxide (SnO2) semiconductor), the remaining being a temperature
sensor (LM35 from National Semiconductor Corporation), a
humidity sensor (HIH–3610 from Honeywell), and a pressure sensor
(XFAM from Fujikura Ltd.).
Our integrated hardware–software system uses some machine
learning principles and least square regression principle to identify at
first a new gas sample, or a mixture, and then to estimate the
concentrations. In particular we adopt a training model using the
Support Vector Machine (SVM) approach with linear kernel to teach
the system how discriminate among different gases. Then we apply
another training model using the least square regression, to predict
the concentrations.
The experimental results demonstrate that the proposed
multiclassification and regression scheme is effective in the
identification of the tested VOCs of methanol and acetone with
96.61% correctness. The concentration prediction is obtained with
0.979 and 0.964 correlation coefficient for the predicted versus real
concentrations of methanol and acetone, respectively.
Abstract: Creative drama which interconnects with the concepts of play, theatre, animation and role playing is a field which can only be learnt and expressed through experiencing. This study about assessment of the drama teaching in preschools by children was conducted in 3 preschools in Ankara with participation of 12 children of 6 ages who had taken drama learning courses. Qualitative research approach and semi-structured interviewing technique were employed. The results of the study indicated that all of 12 children defined drama as a game and entertainment.
Abstract: We present here the results for a comparative study of
some techniques, available in the literature, related to the relevance
feedback mechanism in the case of a short-term learning. Only one
method among those considered here is belonging to the data mining
field which is the K-nearest neighbors algorithm (KNN) while the
rest of the methods is related purely to the information retrieval field
and they fall under the purview of the following three major axes:
Shifting query, Feature Weighting and the optimization of the
parameters of similarity metric. As a contribution, and in addition to
the comparative purpose, we propose a new version of the KNN
algorithm referred to as an incremental KNN which is distinct from
the original version in the sense that besides the influence of the
seeds, the rate of the actual target image is influenced also by the
images already rated. The results presented here have been obtained
after experiments conducted on the Wang database for one iteration
and utilizing color moments on the RGB space. This compact
descriptor, Color Moments, is adequate for the efficiency purposes
needed in the case of interactive systems. The results obtained allow
us to claim that the proposed algorithm proves good results; it even
outperforms a wide range of techniques available in the literature.
Abstract: In this contribution an innovative platform is being
presented that integrates intelligent agents in legacy e-learning environments. It introduces the design and development of a scalable
and interoperable integration platform supporting various assessment agents for e-learning environments. The agents are implemented in
order to provide intelligent assessment services to computational intelligent techniques such as Bayesian Networks and Genetic
Algorithms. The utilization of new and emerging technologies like web services allows integrating the provided services to any web
based legacy e-learning environment.
Abstract: The conjugate gradient optimization algorithm is combined with the modified back propagation algorithm to yield a computationally efficient algorithm for training multilayer perceptron (MLP) networks (CGFR/AG). The computational efficiency is enhanced by adaptively modifying initial search direction as described in the following steps: (1) Modification on standard back propagation algorithm by introducing a gain variation term in the activation function, (2) Calculation of the gradient descent of error with respect to the weights and gains values and (3) the determination of a new search direction by using information calculated in step (2). The performance of the proposed method is demonstrated by comparing accuracy and computation time with the conjugate gradient algorithm used in MATLAB neural network toolbox. The results show that the computational efficiency of the proposed method was better than the standard conjugate gradient algorithm.