Abstract: Faced with social and health system capacity
constraints and rising and changing demand for welfare services,
governments and welfare providers are increasingly relying on
innovation to help support and enhance services. However, the
evidence reported by several studies indicates that the realization of
that potential is not an easy task. Innovations can be deemed
inherently complex to implement and operate, because many of them
involve a combination of technological and organizational renewal
within an environment featuring a diversity of stakeholders. Many
public welfare service innovations are markedly systemic in their
nature, which means that they emerge from, and must address, the
complex interplay between political, administrative, technological,
institutional and legal issues. This paper suggests that stakeholders
dealing with systemic innovation in welfare services must deal with
ambiguous and incomplete information in circumstances of
uncertainty. Employing a literature review methodology and case
study, this paper identifies, categorizes and discusses different
aspects of the uncertainty of systemic innovation in public welfare
services, and argues that uncertainty can be classified into eight
categories: technological uncertainty, market uncertainty,
regulatory/institutional uncertainty, social/political uncertainty,
acceptance/legitimacy uncertainty, managerial uncertainty, timing
uncertainty and consequence uncertainty.
Abstract: The emergence of blended learning has been
influenced by the rapid changes in Higher Education within the last
few years. However, there is a lack of studies that look into the future
of blended learning in the Saudi context. The most likely explanation
is that blended learning is relatively new and, with respect to learning
in general, under-researched. This study addresses this gap and
explores the views of lecturers and students towards the future of
blended learning in Saudi Arabia. This study was informed by the
interpretive paradigm that appears to be most appropriate to
understand and interpret the perceptions of students and instructors
towards a new learning environment. While globally there has been
considerable research on the perceptions of e-learning and blended
learning with its different models, there is plenty of space for further
research specifically in the Arab region, and in Saudi Arabia where
blended learning is now being introduced.
Abstract: A new digital watermarking technique for images that
are sensitive to blocking artifacts is presented. Experimental results
show that the proposed MDCT based approach produces highly
imperceptible watermarked images and is robust to attacks such as
compression, noise, filtering and geometric transformations. The
proposed MDCT watermarking technique is applied to fingerprints
for ensuring security. The face image and demographic text data of
an individual are used as multiple watermarks. An AFIS system was
used to quantitatively evaluate the matching performance of the
MDCT-based watermarked fingerprint. The high fingerprint
matching scores show that the MDCT approach is resilient to
blocking artifacts. The quality of the extracted face and extracted text
images was computed using two human visual system metrics and
the results show that the image quality was high.
Abstract: As the Internet continues to grow at a rapid pace as
the primary medium for communications and commerce and as
telecommunication networks and systems continue to expand their
global reach, digital information has become the most popular and
important information resource and our dependence upon the
underlying cyber infrastructure has been increasing significantly.
Unfortunately, as our dependency has grown, so has the threat to the
cyber infrastructure from spammers, attackers and criminal
enterprises. In this paper, we propose a new machine learning based
network intrusion detection framework for cyber security. The
detection process of the framework consists of two stages: model
construction and intrusion detection. In the model construction stage,
a semi-supervised machine learning algorithm is applied to a
collected set of network audit data to generate a profile of normal
network behavior and in the intrusion detection stage, input network
events are analyzed and compared with the patterns gathered in the
profile, and some of them are then flagged as anomalies should these
events are sufficiently far from the expected normal behavior. The
proposed framework is particularly applicable to the situations where
there is only a small amount of labeled network training data
available, which is very typical in real world network environments.
Abstract: Quality of Service (QoS) Routing aims to find path between source and destination satisfying the QoS requirements which efficiently using the network resources and underlying routing algorithm and to fmd low-cost paths that satisfy given QoS constraints. One of the key issues in providing end-to-end QoS guarantees in packet networks is determining feasible path that satisfies a number of QoS constraints. We present a Optimized Multi- Constrained Routing (OMCR) algorithm for the computation of constrained paths for QoS routing in computer networks. OMCR applies distance vector to construct a shortest path for each destination with reference to a given optimization metric, from which a set of feasible paths are derived at each node. OMCR is able to fmd feasible paths as well as optimize the utilization of network resources. OMCR operates with the hop-by-hop, connectionless routing model in IP Internet and does not create any loops while fmding the feasible paths. Nodes running OMCR not necessarily maintaining global view of network state such as topology, resource information and routing updates are sent only to neighboring nodes whereas its counterpart link-state routing method depend on complete network state for constrained path computation and that incurs excessive communication overhead.
Abstract: This paper outlines the development of a learning retrieval agent. Task of this agent is to extract knowledge of the Active Semantic Network in respect to user-requests. Based on a reinforcement learning approach, the agent learns to interpret the user-s intention. Especially, the learning algorithm focuses on the retrieval of complex long distant relations. Increasing its learnt knowledge with every request-result-evaluation sequence, the agent enhances his capability in finding the intended information.
Abstract: The purpose of this research is to disentangle and
validate the underlying factorial-structure of Ecotourism Experiential
Value (EEV) measurement scale and subsequently investigate its
psychometric properties. The analysis was based on a sample of 225
eco-tourists, collected at the vicinity of Taman Negara National Park
(TNNP) via interviewer-administered questionnaire. Exploratory
factor analysis (EFA) was performed to determine the factorial
structure of EEV. Subsequently, to confirm and validate the factorial
structure and assess the psychometric properties of EEV,
confirmatory factor analysis (CFA) was executed. In addition, to
establish the nomological validity of EEV a structural model was
developed to examine the effect of EEV on Total Eco-tourist
Experience Quality (TEEQ). It is unveiled that EEV is a secondorder
six-factorial structure construct and it scale has adequately met
the psychometric criteria, thus could permit interpretation of results
confidently. The findings have important implications for future
research directions and management of ecotourism destination.
Abstract: Economic crime (i.e. corporate fraud) has a
significant impact on business. This study analyzes the fraud cases
reported by the Malaysian Securities Commission. Frauds involving
market manipulation and/or illegal share trading are the most
common types of fraud reported over the 6 years analyzed. The
highest number of frauds reported involved investment and fund
holding companies. Alarmingly the results indicate quite a high
number of frauds cases are committed by management. The higher
number of Chinese perpetrators may be due to fact that they are the
dominant group in Malaysian business. The result also shows that
more than half of companies involved with fraud are privately held
companies in the investment/fund/finance sector. The results of this
study highlight general characteristic of perpetrators (person and
company) that commit fraud which could help the regulators in their
monitoring and enforcement activities. To investors, this would help
in analyzing their business investment or portfolio risk.
Abstract: The objective of current issue was to develop a model
of testicular herpes simplex virus (HSV) type I infection for
assessment of viral effect on fertility. 56 male mice were inoculated
intraperitoneally with different concentrations of HSV on 8 day post
partum. It was revealed that the optimal dose was 100 plaque
forming units per mice as it provided testicular infection in 100% of
survivors. HSV proteins were detected both in somatic and germ
cells (spermatogonia, spermatocytes, spermatides). Although DNA
load in testis was descending from 3 to 28 days post infection only
12.5% of infected males had offspring after mating with uninfected
females comparing to 87.5% in control (p=0.012). These results are
the first direct evidence for HSV impact in male sterility. Prepuberal
mice appeared to be a suitable model for investigation of
pathogenesis of virus-associated fertility disorders.
Abstract: Compliance requires an effective communication
within an enterprise as well as towards a company-s external
environment. This requirement commences with the
implementation of compliance within large scale compliance
projects and still persists in the compliance reporting within
standard operations. On the one hand the understanding of
compliance necessities within the organization is promoted.
On the other hand reduction of asymmetric information with
compliance stakeholders is achieved. To reach this goal, a
central reporting must provide a consolidated view of different
compliance efforts- statuses. A concept which could be
adapted for this purpose is the balanced scorecard by Kaplan /
Norton. This concept has not been analyzed in detail
concerning its adequacy for a holistic compliance reporting
starting in compliance projects until later usage in regularly
compliance operations.
At first, this paper evaluates if a holistic compliance
reporting can be designed by using the balanced scorecard
concept. The current status of compliance reporting clearly
shows that scorecards are generally accepted as a compliance
reporting tool and are already used for corporate governance
reporting. Additional specialized compliance IT - solutions
exist in the market. After the scorecard-s adequacy is
thoroughly examined and proofed, an example strategy map as
the basis to derive a compliance balanced scorecard is defined.
This definition answers the question on proceeding in
designing a compliance reporting tool.
Abstract: Probabilistic techniques in computer programs are becoming
more and more widely used. Therefore, there is a big
interest in the formal specification, verification, and development
of probabilistic programs. In our work-in-progress project, we are
attempting to make a constructive framework for developing probabilistic
programs formally. The main contribution of this paper
is to introduce an intermediate artifact of our work, a Z-based
formalism called PZ, by which one can build set theoretical models of
probabilistic programs. We propose to use a constructive set theory,
called CZ set theory, to interpret the specifications written in PZ.
Since CZ has an interpretation in Martin-L¨of-s theory of types, this
idea enables us to derive probabilistic programs from correctness
proofs of their PZ specifications.
Abstract: The design of a complete expansion that allows for
compact representation of certain relevant classes of signals is a
central problem in signal processing applications. Achieving such a
representation means knowing the signal features for the purpose of
denoising, classification, interpolation and forecasting. Multilayer
Neural Networks are relatively a new class of techniques that are
mathematically proven to approximate any continuous function
arbitrarily well. Radial Basis Function Networks, which make use of
Gaussian activation function, are also shown to be a universal
approximator. In this age of ever-increasing digitization in the
storage, processing, analysis and communication of information,
there are numerous examples of applications where one needs to
construct a continuously defined function or numerical algorithm to
approximate, represent and reconstruct the given discrete data of a
signal. Many a times one wishes to manipulate the data in a way that
requires information not included explicitly in the data, which is
done through interpolation and/or extrapolation.
Tidal data are a very perfect example of time series and many
statistical techniques have been applied for tidal data analysis and
representation. ANN is recent addition to such techniques. In the
present paper we describe the time series representation capabilities
of a special type of ANN- Radial Basis Function networks and
present the results of tidal data representation using RBF. Tidal data
analysis & representation is one of the important requirements in
marine science for forecasting.
Abstract: In this paper, we explore a new scheme for filtering spoofed packets (DDOS attack) which is a combination of path fingerprint and client puzzle concepts. In this each IP packet has a unique fingerprint is embedded that represents, the route a packet has traversed. The server maintains a mapping table which contains the client IP address and its corresponding fingerprint. In ingress router, client puzzle is placed. For each request, the puzzle issuer provides a puzzle which the source has to solve. Our design has the following advantages over prior approaches, 1) Reduce the network traffic, as we place a client puzzle at the ingress router. 2) Mapping table at the server is lightweight and moderate.
Abstract: Knowledge is indispensable but voluminous knowledge becomes a bottleneck for efficient processing. A great challenge for data mining activity is the generation of large number of potential rules as a result of mining process. In fact sometimes result size is comparable to the original data. Traditional data mining pruning activities such as support do not sufficiently reduce the huge rule space. Moreover, many practical applications are characterized by continual change of data and knowledge, thereby making knowledge voluminous with each change. The most predominant representation of the discovered knowledge is the standard Production Rules (PRs) in the form If P Then D. Michalski & Winston proposed Censored Production Rules (CPRs), as an extension of production rules, that exhibit variable precision and supports an efficient mechanism for handling exceptions. A CPR is an augmented production rule of the form: If P Then D Unless C, where C (Censor) is an exception to the rule. Such rules are employed in situations in which the conditional statement 'If P Then D' holds frequently and the assertion C holds rarely. By using a rule of this type we are free to ignore the exception conditions, when the resources needed to establish its presence, are tight or there is simply no information available as to whether it holds or not. Thus the 'If P Then D' part of the CPR expresses important information while the Unless C part acts only as a switch changes the polarity of D to ~D. In this paper a scheme based on Dempster-Shafer Theory (DST) interpretation of a CPR is suggested for discovering CPRs from the discovered flat PRs. The discovery of CPRs from flat rules would result in considerable reduction of the already discovered rules. The proposed scheme incrementally incorporates new knowledge and also reduces the size of knowledge base considerably with each episode. Examples are given to demonstrate the behaviour of the proposed scheme. The suggested cumulative learning scheme would be useful in mining data streams.
Abstract: In the context of business incubation (BI) as strategic
enablers, this paper critically reviews the literature relating to the
strategic benefits of BI in the Middle East. The taxonomy of BI
benefits in the strategic elements on 1) type, 2) financial model, 3)
services, 4) objectives, 5) number of clients, 6) number of graduates,
and 7) jobs creation. Understanding the importance of BI benefits can
be significant in the economic development although most incubators
lead to diversify the economy. Thus, taxonomies of the benefits of BI
are produced from both the academic literature and published case
studies. In this way, a classification of strategic benefits elements as
they relate to incubators has been developed to provide a greater
understanding of the benefits needed to obtain a specific element.
The result of this paper is Business incubators is aimed
entrepreneurship, jobs creation, research commercialization and
profitable enterprises in Middle Eastern countries.
Abstract: Imaging is defined as the process of obtaining
geometric images either two dimensional or three dimensional by scanning or digitizing the existing objects or products. In this research, it applied to retrieve 3D information of the human skin
surface in medical application. This research focuses on analyzing
and determining volume of leg ulcers using imaging devices. Volume
determination is one of the important criteria in clinical assessment of leg ulcer. The volume and size of the leg ulcer wound will give the
indication on responding to treatment whether healing or worsening.
Different imaging techniques are expected to give different result (and accuracies) in generating data and images. Midpoint projection
algorithm was used to reconstruct the cavity to solid model and compute the volume. Misinterpretation of the results can affect the
treatment efficacy. The objectives of this paper is to compare the
accuracy between two 3D data acquisition method, which is laser
triangulation and structured light methods, It was shown that using models with known volume, that structured-light-based 3D technique
produces better accuracy compared with laser triangulation data
acquisition method for leg ulcer volume determination.
Abstract: Knowledge management is a critical component of
competitive success in service organizations. Knowledge
management centers on creating new knowledge and utilizing
existing knowledge. While utilizing existing knowledge relates to
input and control and can lead to a reduction in costs; creating new
knowledge relates to output and growth and can lead to an increase in
revenue. Therefore managers must ensure that they can successfully
optimize the knowledge and talent in their organizations. To do this
they and must try to develop an environment that promotes the
generation, acquisition, transfer and use of valuable knowledge in
creative ways. However knowledge management is complex and
diverse. Research suggests that organizations in general and SMEs in
particular are finding it difficult to implement successful knowledge
management initiatives. Our research attempts to understand whether
organizations are adopting best practice initiatives in their
organizations. This paper presents findings from an exploratory study
of 139 SMEs operating in the tourism sector across Europe. The
goals of the survey is to assess the level of awareness of knowledge
and talent management strategies and methodologies and to
determine whether the responding companies implement best practice
knowledge management initiatives in their organizations Analysis of
the findings from the study are presented and discussed.
Abstract: A multi-block algorithm and its implementation in two-dimensional finite element numerical model CCHE2D are presented. In addition to a conventional Lagrangian Interpolation Method (LIM), a novel interpolation method, called Consistent Interpolation Method (CIM), is proposed for more accurate information transfer across the interfaces. The consistent interpolation solves the governing equations over the auxiliary elements constructed around the interpolation nodes using the same numerical scheme used for the internal computational nodes. With the CIM, the momentum conservation can be maintained as well as the mass conservation. An imbalance correction scheme is used to enforce the conservation laws (mass and momentum) across the interfaces. Comparisons of the LIM and the CIM are made using several flow simulation examples. It is shown that the proposed CIM is physically more accurate and produces satisfactory results efficiently.
Abstract: A finite element analysis was conducted to determine
the effect of moisture diffusion and hygroscopic swelling in rice. A
parallel simple stochastic modeling was performed to predict the
number of grains cracked as a result of moisture absorption and
hygroscopic swelling. Rice grains were soaked in thermally (25 oC)
controlled water and then tested for compressive stress. The
destructive compressive stress tests revealed through compressive
stress calculation that the peak force required to cause cracking in
grains soaked in water reduced with time as soaking duration was
extended. Results of the experiment showed that several grains had
their value of the predicted compressive stress below the von Mises
stress and were interpreted as grains which become cracked and/or
broke during soaking. The technique developed in this experiment
will facilitate the approximation of the number of grains which will
crack during soaking.
Abstract: A biocompatible ferrofluid have been prepared by coprecipitation
of FeCl2.4H2O and FeCl3.6H2O under ultrasonic
irradiation and with NaOH as alkaline agent. Cystein was also used
as capping agent in the solution. Magnetic properties of the produced
ferrofluid were then determined by VSM test and magnetite
nanoparticles were characterized by XRD and TEM techniques. The
effect of surfactant to Fe ion weight ratio was also studied during this
project by using two different amount of Dextran. Results showed the
presence of a biocompatible superparamagnetic ferrofluid including
magnetite nanoparticles with particle size ranging under 20 nm. The
increase in the surfactant content results in the narrowing of the size
distribution and reduction of the particle size and more solution
stability.