Abstract: Chaos and fractals are novel fields of physics and mathematics showing up a new way of universe viewpoint and creating many ideas to solve several present problems. In this paper, a novel algorithm based on the chaotic sequence generator with the highest ability to adapt and reach the global optima is proposed. The adaptive ability of proposal algorithm is flexible in 2 steps. The first one is a breadth-first search and the second one is a depth-first search. The proposal algorithm is examined by 2 functions, the Camel function and the Schaffer function. Furthermore, the proposal algorithm is applied to optimize training Multilayer Neural Networks.
Abstract: In the recent past Learning Classifier Systems have
been successfully used for data mining. Learning Classifier System
(LCS) is basically a machine learning technique which combines
evolutionary computing, reinforcement learning, supervised or
unsupervised learning and heuristics to produce adaptive systems. A
LCS learns by interacting with an environment from which it
receives feedback in the form of numerical reward. Learning is
achieved by trying to maximize the amount of reward received. All
LCSs models more or less, comprise four main components; a finite
population of condition–action rules, called classifiers; the
performance component, which governs the interaction with the
environment; the credit assignment component, which distributes the
reward received from the environment to the classifiers accountable
for the rewards obtained; the discovery component, which is
responsible for discovering better rules and improving existing ones
through a genetic algorithm. The concatenate of the production rules
in the LCS form the genotype, and therefore the GA should operate
on a population of classifier systems. This approach is known as the
'Pittsburgh' Classifier Systems. Other LCS that perform their GA at
the rule level within a population are known as 'Mitchigan' Classifier
Systems. The most predominant representation of the discovered
knowledge is the standard production rules (PRs) in the form of IF P
THEN D. The PRs, however, are unable to handle exceptions and do
not exhibit variable precision. The Censored Production Rules
(CPRs), an extension of PRs, were proposed by Michalski and
Winston 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
Censor C is an exception to the rule. Such rules are employed in
situations, in which 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 CPR expresses important information, while the
UNLESS C part acts only as a switch and changes the polarity of D
to ~D. In this paper Pittsburgh style LCSs approach is used for
automated discovery of CPRs. An appropriate encoding scheme is
suggested to represent a chromosome consisting of fixed size set of
CPRs. Suitable genetic operators are designed for the set of CPRs
and individual CPRs and also appropriate fitness function is proposed
that incorporates basic constraints on CPR. Experimental results are
presented to demonstrate the performance of the proposed learning
classifier system.
Abstract: In the past few years there is a change in the view of high performance applications and parallel computing. Initially such applications were targeted towards dedicated parallel machines. Recently trend is changing towards building meta-applications composed of several modules that exploit heterogeneous platforms and employ hybrid forms of parallelism. The aim of this paper is to propose a model of virtual parallel computing. Virtual parallel computing system provides a flexible object oriented software framework that makes it easy for programmers to write various parallel applications.
Abstract: Trust management and Reputation models are
becoming integral part of Internet based applications such as CSCW,
E-commerce and Grid Computing. Also the trust dimension is a
significant social structure and key to social relations within a
collaborative community. Collaborative Decision Making (CDM) is
a difficult task in the context of distributed environment (information
across different geographical locations) and multidisciplinary
decisions are involved such as Virtual Organization (VO). To aid
team decision making in VO, Decision Support System and social
network analysis approaches are integrated. In such situations social
learning helps an organization in terms of relationship, team
formation, partner selection etc. In this paper we focus on trust
learning. Trust learning is an important activity in terms of
information exchange, negotiation, collaboration and trust
assessment for cooperation among virtual team members. In this
paper we have proposed a reinforcement learning which enhances the
trust decision making capability of interacting agents during
collaboration in problem solving activity. Trust computational model
with learning that we present is adapted for best alternate selection of
new project in the organization. We verify our model in a multi-agent
simulation where the agents in the community learn to identify
trustworthy members, inconsistent behavior and conflicting behavior
of agents.
Abstract: We have previously introduced an ultrasonic imaging
approach that combines harmonic-sensitive pulse sequences with a
post-beamforming quadratic kernel derived from a second-order
Volterra filter (SOVF). This approach is designed to produce images
with high sensitivity to nonlinear oscillations from microbubble
ultrasound contrast agents (UCA) while maintaining high levels of
noise rejection. In this paper, a two-step algorithm for computing the
coefficients of the quadratic kernel leading to reduction of tissue
component introduced by motion, maximizing the noise rejection and
increases the specificity while optimizing the sensitivity to the UCA
is presented. In the first step, quadratic kernels from individual
singular modes of the PI data matrix are compared in terms of their
ability of maximize the contrast to tissue ratio (CTR). In the second
step, quadratic kernels resulting in the highest CTR values are
convolved. The imaging results indicate that a signal processing
approach to this clinical challenge is feasible.
Abstract: IETF defines mobility support in IPv6, i.e. MIPv6, to
allow nodes to remain reachable while moving around in the IPv6
internet. When a node moves and visits a foreign network, it is still
reachable through the indirect packet forwarding from its home
network. This triangular routing feature provides node mobility but
increases the communication latency between nodes. This deficiency
can be overcome by using a Binding Update (BU) scheme, which let
nodes keep up-to-date IP addresses and communicate with each other
through direct IP routing. To further protect the security of BU, a
Return Routability (RR) procedure was developed. However, it has
been found that RR procedure is vulnerable to many attacks. In this
paper, we will propose a lightweight RR procedure based on
geometric computing. In consideration of the inherent limitation of
computing resources in mobile node, the proposed scheme is
developed to minimize the cost of computations and to eliminate the
overhead of state maintenance during binding updates. Compared with
other CGA-based BU schemes, our scheme is more efficient and
doesn-t need nonce tables in nodes.
Abstract: In this paper, we have developed a method to
compute fractal dimension (FD) of discrete time signals, in the
time domain, by modifying the box-counting method. The size
of the box is dependent on the sampling frequency of the
signal. The number of boxes required to completely cover the
signal are obtained at multiple time resolutions. The time
resolutions are made coarse by decimating the signal. The loglog
plot of total number of boxes required to cover the curve
versus size of the box used appears to be a straight line, whose
slope is taken as an estimate of FD of the signal. The results
are provided to demonstrate the performance of the proposed
method using parametric fractal signals. The estimation
accuracy of the method is compared with that of Katz, Sevcik,
and Higuchi methods. In addition, some properties of the FD
are discussed.
Abstract: Modular multiplication is the basic operation
in most public key cryptosystems, such as RSA, DSA, ECC,
and DH key exchange. Unfortunately, very large operands
(in order of 1024 or 2048 bits) must be used to provide
sufficient security strength. The use of such big numbers
dramatically slows down the whole cipher system, especially
when running on embedded processors.
So far, customized hardware accelerators - developed on
FPGAs or ASICs - were the best choice for accelerating
modular multiplication in embedded environments. On the
other hand, many algorithms have been developed to speed
up such operations. Examples are the Montgomery modular
multiplication and the interleaved modular multiplication
algorithms. Combining both customized hardware with
an efficient algorithm is expected to provide a much faster
cipher system.
This paper introduces an enhanced architecture for computing
the modular multiplication of two large numbers X
and Y modulo a given modulus M. The proposed design is
compared with three previous architectures depending on
carry save adders and look up tables. Look up tables should
be loaded with a set of pre-computed values. Our proposed
architecture uses the same carry save addition, but replaces
both look up tables and pre-computations with an enhanced
version of sign detection techniques. The proposed architecture
supports higher frequencies than other architectures.
It also has a better overall absolute time for a single operation.
Abstract: A real time distributed computing has
heterogeneously networked computers to solve a single problem. So
coordination of activities among computers is a complex task and
deadlines make more complex. The performances depend on many
factors such as traffic workloads, database system architecture,
underlying processors, disks speeds, etc. Simulation study have been
performed to analyze the performance under different transaction
scheduling: different workloads, arrival rate, priority policies,
altering slack factors and Preemptive Policy. The performance metric
of the experiments is missed percent that is the percentage of
transaction that the system is unable to complete. The throughput of
the system is depends on the arrival rate of transaction. The
performance can be enhanced with altering the slack factor value.
Working on slack value for the transaction can helps to avoid some
of transactions from killing or aborts. Under the Preemptive Policy,
many extra executions of new transactions can be carried out.
Abstract: System is using multiple processors for computing and information processing, is increasing rapidly speed operation of these systems compared with single processor systems, very significant impact on system performance is increased .important differences to yield a single multi-processor cpu, the scheduling policies, to reduce the implementation time of all processes. Notwithstanding the famous algorithms such as SPT, LPT, LSPT and RLPT for scheduling and there, but none led to the answer are not optimal.In this paper scheduling using genetic algorithms and innovative way to finish the whole process faster that we do and the result compared with three algorithms we mentioned.
Abstract: Distance visualization of large datasets often takes the direction of remote viewing and zooming techniques of stored static images. However, the continuous increase in the size of datasets and visualization operation causes insufficient performance with traditional desktop computers. Additionally, the visualization techniques such as Isosurface depend on the available resources of the running machine and the size of datasets. Moreover, the continuous demand for powerful computing powers and continuous increase in the size of datasets results an urgent need for a grid computing infrastructure. However, some issues arise in current grid such as resources availability at the client machines which are not sufficient enough to process large datasets. On top of that, different output devices and different network bandwidth between the visualization pipeline components often result output suitable for one machine and not suitable for another. In this paper we investigate how the grid services could be used to support remote visualization of large datasets and to break the constraint of physical co-location of the resources by applying the grid computing technologies. We show our grid enabled architecture to visualize large medical datasets (circa 5 million polygons) for remote interactive visualization on modest resources clients.
Abstract: Many-core GPUs provide high computing ability and
substantial bandwidth; however, optimizing irregular applications
like SpMV on GPUs becomes a difficult but meaningful task. In this
paper, we propose a novel method to improve the performance of
SpMV on GPUs. A new storage format called HYB-R is proposed to
exploit GPU architecture more efficiently. The COO portion of the
matrix is partitioned recursively into a ELL portion and a COO
portion in the process of creating HYB-R format to ensure that there
are as many non-zeros as possible in ELL format. The method of
partitioning the matrix is an important problem for HYB-R kernel, so
we also try to tune the parameters to partition the matrix for higher
performance. Experimental results show that our method can get
better performance than the fastest kernel (HYB) in NVIDIA-s
SpMV library with as high as 17% speedup.
Abstract: The goal of data mining algorithms is to discover
useful information embedded in large databases. One of the most
important data mining problems is discovery of frequently occurring
patterns in sequential data. In a multidimensional sequence each
event depends on more than one dimension. The search space is quite
large and the serial algorithms are not scalable for very large
datasets. To address this, it is necessary to study scalable parallel
implementations of sequence mining algorithms.
In this paper, we present a model for multidimensional sequence
and describe a parallel algorithm based on data parallelism.
Simulation experiments show good load balancing and scalable and
acceptable speedup over different processors and problem sizes and
demonstrate that our approach can works efficiently in a real parallel
computing environment.
Abstract: The Economic factors are leading to the rise of
infrastructures provides software and computing facilities as a
service, known as cloud services or cloud computing. Cloud services
can provide efficiencies for application providers, both by limiting
up-front capital expenses, and by reducing the cost of ownership over
time. Such services are made available in a data center, using shared
commodity hardware for computation and storage. There is a varied
set of cloud services available today, including application services
(salesforce.com), storage services (Amazon S3), compute services
(Google App Engine, Amazon EC2) and data services (Amazon
SimpleDB, Microsoft SQL Server Data Services, Google-s Data
store). These services represent a variety of reformations of data
management architectures, and more are on the horizon.
Abstract: Wireless sensor networks (WSN) consists of many sensor nodes that are placed on unattended environments such as military sites in order to collect important information. Implementing a secure protocol that can prevent forwarding forged data and modifying content of aggregated data and has low delay and overhead of communication, computing and storage is very important. This paper presents a new protocol for concealed data aggregation (CDA). In this protocol, the network is divided to virtual cells, nodes within each cell produce a shared key to send and receive of concealed data with each other. Considering to data aggregation in each cell is locally and implementing a secure authentication mechanism, data aggregation delay is very low and producing false data in the network by malicious nodes is not possible. To evaluate the performance of our proposed protocol, we have presented computational models that show the performance and low overhead in our protocol.
Abstract: Imperfect knowledge cannot be avoided all the time. Imperfections may have several forms; uncertainties, imprecision and incompleteness. When we look to classification of methods for the management of imperfect knowledge we see fuzzy set-based techniques. The choice of a method to process data is linked to the choice of knowledge representation, which can be numerical, symbolic, logical or semantic and it depends on the nature of the problem to be solved for example decision support, which will be mentioned in our study. Fuzzy Logic is used for its ability to manage imprecise knowledge, but it can take advantage of the ability of neural networks to learn coefficients or functions. Such an association of methods is typical of so-called soft computing. In this study a new method was used for the management of imprecision for collected knowledge which related to economic analysis of construction industry in Turkey. Because of sudden changes occurring in economic factors decrease competition strength of construction companies. The better evaluation of these changes in economical factors in view of construction industry will made positive influence on company-s decisions which are dealing construction.
Abstract: Grid computing is a high performance computing
environment to solve larger scale computational applications. Grid
computing contains resource management, job scheduling, security
problems, information management and so on. Job scheduling is a
fundamental and important issue in achieving high performance in
grid computing systems. However, it is a big challenge to design an
efficient scheduler and its implementation. In Grid Computing, there
is a need of further improvement in Job Scheduling algorithm to
schedule the light-weight or small jobs into a coarse-grained or
group of jobs, which will reduce the communication time,
processing time and enhance resource utilization. This Grouping
strategy considers the processing power, memory-size and
bandwidth requirements of each job to realize the real grid system.
The experimental results demonstrate that the proposed scheduling
algorithm efficiently reduces the processing time of jobs in
comparison to others.
Abstract: Accurate assessment of the primary tumor response to
treatment is important in the management of breast cancer. This
paper introduces a new set of treatment evaluation indicators for
breast cancer cases based on the computational process of three
known metrics, the Euclidian, Hamming and Levenshtein distances.
The distance principals are applied to pairs of mammograms and/or
echograms, recorded before and after treatment, determining a
reference point in judging the evolution amount of the studied
carcinoma. The obtained numerical results are indeed very
transparent and indicate not only the evolution or the involution of
the tumor under treatment, but also a quantitative measurement of the
benefit in using the selected method of treatment.
Abstract: Cloud computing is the innovative and leading
information technology model for enabling convenient, on-demand
network access to a shared pool of configurable computing resources
that can be rapidly provisioned and released with minimal
management effort. This paper presents our development on enabling
an individual user's desktop in a virtualized environment, which is
stored on a remote virtual machine rather than locally. We present the
initial work on the integration of virtual desktop and application
sharing with virtualization technology. Given the development of
remote desktop virtualization, this proposed effort has the potential to
positively provide an efficient, resilience and elastic environment for
online cloud service. Users no longer need to burden the cost of
software licenses and platform maintenances. Moreover, this
development also helps boost user productivity by promoting a
flexible model that lets users access their desktop environments from
virtually anywhere.
Abstract: This paper presents the benchmarking results and
performance evaluation of differentclustersbuilt atthe National Center
for High-Performance Computingin Taiwan. Performance of
processor, memory subsystem andinterconnect is a critical factor in the
overall performance of high performance computing platforms. The
evaluation compares different system architecture and software
platforms. Most supercomputer used HPL to benchmark their system
performance, in accordance with the requirement of the TOP500 List.
In this paper we consider system memory access factors that affect
benchmark performance, such as processor and memory
performance.We hope these works will provide useful information for
future development and construct cluster system.