Abstract: Large scale systems such as computational Grid is
a distributed computing infrastructure that can provide globally
available network resources. The evolution of information processing
systems in Data Grid is characterized by a strong decentralization of
data in several fields whose objective is to ensure the availability and
the reliability of the data in the reason to provide a fault tolerance
and scalability, which cannot be possible only with the use of the
techniques of replication. Unfortunately the use of these techniques
has a height cost, because it is necessary to maintain consistency
between the distributed data. Nevertheless, to agree to live with
certain imperfections can improve the performance of the system by
improving competition. In this paper, we propose a multi-layer protocol
combining the pessimistic and optimistic approaches conceived
for the data consistency maintenance in large scale systems. Our
approach is based on a hierarchical representation model with tree
layers, whose objective is with double vocation, because it initially
makes it possible to reduce response times compared to completely
pessimistic approach and it the second time to improve the quality
of service compared to an optimistic approach.
Abstract: In the queueing theory, it is assumed that customer
arrivals correspond to a Poisson process and service time has the
exponential distribution. Using these assumptions, the behaviour of
the queueing system can be described by means of Markov chains
and it is possible to derive the characteristics of the system. In the
paper, these theoretical approaches are presented on several types of
systems and it is also shown how to compute the characteristics in a
situation when these assumptions are not satisfied
Abstract: Unsatisfactory effectiveness of software systems
development and enhancement projects is one of the main reasons
why in software engineering there are attempts being made to use
experiences coming from other engineering disciplines. In spite of
specificity of software product and process a belief had come out that
the execution of software could be more effective if these objects
were subject to measurement – as it is true in other engineering
disciplines for which measurement is an immanent feature. Thus
objective and reliable approaches to the measurement of software
processes and products have been sought in software engineering for
several dozens of years already. This may be proved, among others,
by the current version of CMMI for Development model. This paper
is aimed at analyzing the approach to the software processes and
products measurement proposed in the latest version of this very
model, indicating growing acceptance for this issue in software
engineering.
Abstract: In this paper, the main principles of text-to-speech synthesis system are presented. Associated problems which arise when developing speech synthesis system are described. Used approaches and their application in the speech synthesis systems for Azerbaijani language are shown.
Abstract: E-learning is not restricted to the use of new technologies for the online content, but also induces the adoption of new approaches to improve the quality of education. This quality depends on the ability of these approaches (technical and pedagogical) to provide an adaptive learning environment. Thus, the environment should include features that convey intentions and meeting the educational needs of learners by providing a customized learning path to acquiring a competency concerned In our proposal, we believe that an individualized learning path requires knowledge of the learner. Therefore, it must pass through a personalization of diagnosis to identify precisely the competency gaps to fill, and reduce the cognitive load To personalize the diagnosis and pertinently measure the competency gap, we suggest implementing the formative assessment in the e-learning environment and we propose the introduction of a pre-regulation process in the area of formative assessment, involving its individualization and implementation in e-learning.
Abstract: Recognizing behavioral patterns of financial markets
is essential for traders. Japanese candlestick chart is a common tool to
visualize and analyze such patterns in an economic time series. Since
the world was introduced to Japanese candlestick charting, traders
saw how combining this tool with intelligent technical approaches
creates a powerful formula for the savvy investors.
This paper propose a generalization to box counting method of
Grassberger-Procaccia, which is based on computing the correlation
dimension of Japanese candlesticks instead commonly used 'close'
points. The results of this method applied on several foreign
exchange rates vs. IRR (Iranian Rial). Satisfactorily show lower
chaotic dimension of Japanese candlesticks series than regular
Grassberger-Procaccia method applied merely on close points of
these same candles. This means there is some valuable information
inside candlesticks.
Abstract: Tandem mass spectrometry (MS/MS) is the engine
driving high-throughput protein identification. Protein mixtures possibly
representing thousands of proteins from multiple species are
treated with proteolytic enzymes, cutting the proteins into smaller
peptides that are then analyzed generating MS/MS spectra. The
task of determining the identity of the peptide from its spectrum
is currently the weak point in the process. Current approaches to de
novo sequencing are able to compute candidate peptides efficiently.
The problem lies in the limitations of current scoring functions. In this
paper we introduce the concept of proteome signature. By examining
proteins and compiling proteome signatures (amino acid usage) it is
possible to characterize likely combinations of amino acids and better
distinguish between candidate peptides. Our results strongly support
the hypothesis that a scoring function that considers amino acid usage
patterns is better able to distinguish between candidate peptides. This
in turn leads to higher accuracy in peptide prediction.
Abstract: Bootstrapping has gained popularity in different tests of hypotheses as an alternative in using asymptotic distribution if one is not sure of the distribution of the test statistic under a null hypothesis. This method, in general, has two variants – the parametric and the nonparametric approaches. However, issues on reliability of this method always arise in many applications. This paper addresses the issue on reliability by establishing a reliability measure in terms of quantiles with respect to asymptotic distribution, when this is approximately correct. The test of hypotheses used is Ftest. The simulated results show that using nonparametric bootstrapping in F-test gives better reliability than parametric bootstrapping with relatively higher degrees of freedom.
Abstract: The paper structures research approaches to the crisis
and its management. It focuses on approaches – psychological,
sociological, economic, ethical and technological. Furthermore, it
describes the basic features of models chosen according to those
approaches. By their comparison it shows how the crisis influences
organizations and individuals, and their mutual interaction.
Abstract: This paper presents a modified version of the
maximum urgency first scheduling algorithm. The maximum
urgency algorithm combines the advantages of fixed and dynamic
scheduling to provide the dynamically changing systems with
flexible scheduling. This algorithm, however, has a major
shortcoming due to its scheduling mechanism which may cause a
critical task to fail. The modified maximum urgency first scheduling
algorithm resolves the mentioned problem. In this paper, we propose
two possible implementations for this algorithm by using either
earliest deadline first or modified least laxity first algorithms for
calculating the dynamic priorities. These two approaches are
compared together by simulating the two algorithms. The earliest
deadline first algorithm as the preferred implementation is then
recommended. Afterwards, we make a comparison between our
proposed algorithm and maximum urgency first algorithm using
simulation and results are presented. It is shown that modified
maximum urgency first is superior to maximum urgency first, since it
usually has less task preemption and hence, less related overhead. It
also leads to less failed non-critical tasks in overloaded situations.
Abstract: Due to the fact that in the new century customers tend
to express globally increasing demands, networks of interconnected
businesses have been established in societies and the management of
such networks seems to be a major key through gaining competitive
advantages. Supply chain management encompasses such managerial
activities. Within a supply chain, a critical role is played by quality.
QFD is a widely-utilized tool which serves the purpose of not only
bringing quality to the ultimate provision of products or service
packages required by the end customer or the retailer, but it can also
initiate us into a satisfactory relationship with our initial customer;
that is the wholesaler. However, the wholesalers- cooperation is
considerably based on the capabilities that are heavily dependent on
their locations and existing circumstances. Therefore, it is undeniable
that for all companies each wholesaler possesses a specific
importance ratio which can heavily influence the figures calculated in
the House of Quality in QFD. Moreover, due to the competitiveness
of the marketplace today, it-s been widely recognized that
consumers- expression of demands has been highly volatile in
periods of production. Apparently, such instability and proneness to
change has been very tangibly noticed and taking it into account
during the analysis of HOQ is widely influential and doubtlessly
required. For a more reliable outcome in such matters, this article
demonstrates the application viability of Analytic Network Process
for considering the wholesalers- reputation and simultaneously
introduces a mortality coefficient for the reliability and stability of
the consumers- expressed demands in course of time. Following to
this, the paper provides further elaboration on the relevant
contributory factors and approaches through the calculation of such
coefficients. In the end, the article concludes that an empirical
application is needed to achieve broader validity.
Abstract: The area of Project Risk Management (PRM) has
been extensively researched, and the utilization of various tools and
techniques for managing risk in several industries has been
sufficiently reported. Formal and systematic PRM practices have
been made available for the construction industry. Based on such
body of knowledge, this paper tries to find out the global picture of
PRM practices and approaches with the help of a survey to look into
the usage of PRM techniques and diffusion of software tools, their
level of maturity, and their usefulness in the construction sector.
Results show that, despite existing techniques and tools, their usage is
limited: software tools are used only by a minority of respondents
and their cost is one of the largest hurdles in adoption. Finally, the
paper provides some important guidelines for future research
regarding quantitative risk analysis techniques and suggestions for
PRM software tools development and improvement.
Abstract: Verification of real-time software systems can be
expensive in terms of time and resources. Testing is the main method
of proving correctness but has been shown to be a long and time
consuming process. Everyday engineers are usually unwilling to
adopt formal approaches to correctness because of the overhead
associated with developing their knowledge of such techniques.
Performance modelling techniques allow systems to be evaluated
with respect to timing constraints. This paper describes PARTES, a
framework which guides the extraction of performance models from
programs written in an annotated subset of C.
Abstract: Governments around the world are expending
considerable time and resources framing strategies and policies to
deliver energy security. The term 'energy security' has quietly
slipped into the energy lexicon without any meaningful discourse
about its meaning or assumptions. An examination of explicit and
inferred definitions finds that the concept is inherently slippery
because it is polysemic in nature having multiple dimensions and
taking on different specificities depending on the country (or
continent), timeframe or energy source to which it is applied. But
what does this mean for policymakers? Can traditional policy
approaches be used to address the problem of energy security or does
its- polysemic qualities mean that it should be treated as a 'wicked'
problem? To answer this question, the paper assesses energy security
against nine commonly cited characteristics of wicked policy
problems and finds strong evidence of 'wickedness'.
Abstract: Arbitrarily shaped video objects are an important
concept in modern video coding methods. The techniques presently
used are not based on image elements but rather video objects having
an arbitrary shape. In this paper, spatial shape error concealment
techniques to be used for object-based image in error-prone
environments are proposed. We consider a geometric shape
representation consisting of the object boundary, which can be
extracted from the α-plane. Three different approaches are used to
replace a missing boundary segment: Bézier interpolation, Bézier
approximation and NURBS approximation. Experimental results on
object shape with different concealment difficulty demonstrate the
performance of the proposed methods. Comparisons with proposed
methods are also presented.
Abstract: Reverse engineering of full-genomic interaction networks based on compendia of expression data has been successfully applied for a number of model organisms. This study adapts these approaches for an important non-model organism: The major human fungal pathogen Candida albicans. During the infection process, the pathogen can adapt to a wide range of environmental niches and reversibly changes its growth form. Given the importance of these processes, it is important to know how they are regulated. This study presents a reverse engineering strategy able to infer fullgenomic interaction networks for C. albicans based on a linear regression, utilizing the sparseness criterion (LASSO). To overcome the limited amount of expression data and small number of known interactions, we utilize different prior-knowledge sources guiding the network inference to a knowledge driven solution. Since, no database of known interactions for C. albicans exists, we use a textmining system which utilizes full-text research papers to identify known regulatory interactions. By comparing with these known regulatory interactions, we find an optimal value for global modelling parameters weighting the influence of the sparseness criterion and the prior-knowledge. Furthermore, we show that soft integration of prior-knowledge additionally improves the performance. Finally, we compare the performance of our approach to state of the art network inference approaches.
Abstract: In the current economy of increasing global
competition, many organizations are attempting to use knowledge as
one of the means to gain sustainable competitive advantage. Besides
large organizations, the success of SMEs can be linked to how well
they manage their knowledge. Despite the profusion of research
about knowledge management within large organizations, fewer
studies tried to analyze KM in SMEs.
This research proposes a new framework showing the determinant
role of organizational dimensions onto KM approaches. The paper
and its propositions are based on a literature review and analysis.
In this research, personalization versus codification,
individualization versus institutionalization and IT-based versus non
IT-based are highlighted as three distinct dimensions of knowledge
management approaches.
The study contributes to research by providing a more nuanced
classification of KM approaches and provides guidance to managers
about the types of KM approaches that should be adopted based on
the size, geographical dispersion and task nature of SMEs.
To the author-s knowledge, the paper is the first of its kind to
examine if there are suitable configurations of KM approaches for
SMEs with different dimensions. It gives valuable information, which
hopefully will help SME sector to accomplish KM.
Abstract: Characteristics of ad hoc networks and even their existence depend on the nodes forming them. Thus, services and applications designed for ad hoc networks should adapt to this dynamic and distributed environment. In particular, multicast algorithms having reliability and scalability requirements should abstain from centralized approaches. We aspire to define a reliable and scalable multicast protocol for ad hoc networks. Our target is to utilize epidemic techniques for this purpose. In this paper, we present a brief survey of epidemic algorithms for reliable multicasting in ad hoc networks, and describe formulations and analytical results for simple epidemics. Then, P2P anti-entropy algorithm for content distribution and our prototype simulation model are described together with our initial results demonstrating the behavior of the algorithm.
Abstract: In this paper an approaches for increasing the
effectiveness of error detection in computer network channels with
Pulse-Amplitude Modulation (PAM) has been proposed. Proposed
approaches are based on consideration of special feature of errors,
which are appearances in line with PAM. The first approach consists
of CRC modification specifically for line with PAM. The second
approach is base of weighted checksums using. The way for
checksum components coding has been developed. It has been shown
that proposed checksum modification ensure superior digital data
control transformation reliability for channels with PAM in compare
to CRC.
Abstract: This paper proposes an innovative methodology for
Acceptance Sampling by Variables, which is a particular category of
Statistical Quality Control dealing with the assurance of products
quality. Our contribution lies in the exploitation of machine learning
techniques to address the complexity and remedy the drawbacks of
existing approaches. More specifically, the proposed methodology
exploits Artificial Neural Networks (ANNs) to aid decision making
about the acceptance or rejection of an inspected sample. For any
type of inspection, ANNs are trained by data from corresponding
tables of a standard-s sampling plan schemes. Once trained, ANNs
can give closed-form solutions for any acceptance quality level and
sample size, thus leading to an automation of the reading of the
sampling plan tables, without any need of compromise with the
values of the specific standard chosen each time. The proposed
methodology provides enough flexibility to quality control engineers
during the inspection of their samples, allowing the consideration of
specific needs, while it also reduces the time and the cost required for
these inspections. Its applicability and advantages are demonstrated
through two numerical examples.