Abstract: This paper objects to extend Jon Kleinberg-s research. He introduced the structure of small-world in a grid and shows with a greedy algorithm using only local information able to find route between source and target in delivery time O(log2n). His fundamental model for distributed system uses a two-dimensional grid with longrange random links added between any two node u and v with a probability proportional to distance d(u,v)-2. We propose with an additional information of the long link nearby, we can find the shorter path. We apply the ant colony system as a messenger distributed their pheromone, the long-link details, in surrounding area. The subsequence forwarding decision has more option to move to, select among local neighbors or send to node has long link closer to its target. Our experiment results sustain our approach, the average routing time by Color Pheromone faster than greedy method.
Abstract: Since dealing with high dimensional data is
computationally complex and sometimes even intractable, recently
several feature reductions methods have been developed to reduce
the dimensionality of the data in order to simplify the calculation
analysis in various applications such as text categorization, signal
processing, image retrieval, gene expressions and etc. Among feature
reduction techniques, feature selection is one the most popular
methods due to the preservation of the original features.
In this paper, we propose a new unsupervised feature selection
method which will remove redundant features from the original
feature space by the use of probability density functions of various
features. To show the effectiveness of the proposed method, popular
feature selection methods have been implemented and compared.
Experimental results on the several datasets derived from UCI
repository database, illustrate the effectiveness of our proposed
methods in comparison with the other compared methods in terms of
both classification accuracy and the number of selected features.
Abstract: There are many problems associated with the World Wide
Web: getting lost in the hyperspace; the web content is still accessible only
to humans and difficulties of web administration. The solution to these
problems is the Semantic Web which is considered to be the extension
for the current web presents information in both human readable and
machine processable form. The aim of this study is to reach new
generic foundation architecture for the Semantic Web because there
is no clear architecture for it, there are four versions, but still up to
now there is no agreement for one of these versions nor is there a
clear picture for the relation between different layers and
technologies inside this architecture. This can be done depending on
the idea of previous versions as well as Gerber-s evaluation method
as a step toward an agreement for one Semantic Web architecture.
Abstract: Emotion recognition is an important research field that finds lots of applications nowadays. This work emphasizes on recognizing different emotions from speech signal. The extracted features are related to statistics of pitch, formants, and energy contours, as well as spectral, perceptual and temporal features, jitter, and shimmer. The Artificial Neural Networks (ANN) was chosen as the classifier. Working on finding a robust and fast ANN classifier suitable for different real life application is our concern. Several experiments were carried out on different ANN to investigate the different factors that impact the classification success rate. Using a database containing 7 different emotions, it will be shown that with a proper and careful adjustment of features format, training data sorting, number of features selected and even the ANN type and architecture used, a success rate of 85% or even more can be achieved without increasing the system complicity and the computation time
Abstract: The COSvd Ciphers has been proposed by Filiol and others (2004). It is a strengthened version of COS stream cipher family denoted COSvd that has been adopted for at least one commercial standard. We propose a distinguish attack on this version, and prove that, it is distinguishable from a random stream. In the COSvd Cipher used one S-Box (10×8) on the final part of cipher. We focus on S-Box and use weakness this S-Box for distinguish attack. In addition, found a leak on HNLL that the sub s-boxes don-t select uniformly. We use this property for an Improve distinguish attack.
Abstract: One problem in evaluating recent computational models of human category learning is that there is no standardized method for systematically comparing the models' assumptions or hypotheses. In the present study, a flexible general model (called GECLE) is introduced that can be used as a framework to systematically manipulate and compare the effects and descriptive validities of a limited number of assumptions at a time. Two example simulation studies are presented to show how the GECLE framework can be useful in the field of human high-order cognition research.
Abstract: Key management is a vital component in any modern security protocol. Due to scalability and practical implementation considerations automatic key management seems a natural choice in significantly large virtual private networks (VPNs). In this context IETF Internet Key Exchange (IKE) is the most promising protocol under permanent review. We have made a humble effort to pinpoint IKEv2 net gain over IKEv1 due to recent modifications in its original structure, along with a brief overview of salient improvements between the two versions. We have used US National Institute of Technology NIIST VPN simulator to get some comparisons of important performance metrics.
Abstract: Natural outdoor scene classification is active and
promising research area around the globe. In this study, the
classification is carried out in two phases. In the first phase, the
features are extracted from the images by wavelet decomposition
method and stored in a database as feature vectors. In the second
phase, the neural classifiers such as back-propagation neural network
(BPNN) and resilient back-propagation neural network (RPNN) are
employed for the classification of scenes. Four hundred color images
are considered from MIT database of two classes as forest and street.
A comparative study has been carried out on the performance of the
two neural classifiers BPNN and RPNN on the increasing number of
test samples. RPNN showed better classification results compared to
BPNN on the large test samples.
Abstract: We introduce a novel approach to measuring how
humans learn based on techniques from information theory and
apply it to the oriental game of Go. We show that the total amount
of information observable in human strategies, called the strategic
information, remains constant for populations of players of differing
skill levels for well studied patterns of play. This is despite the very
large amount of knowledge required to progress from the recreational
players at one end of our spectrum to the very best and most
experienced players in the world at the other and is in contrast to
the idea that having more knowledge might imply more 'certainty'
in what move to play next. We show this is true for very local
up to medium sized board patterns, across a variety of different
moves using 80,000 game records. Consequences for theoretical and
practical AI are outlined.
Abstract: The lack of any centralized infrastructure in mobile ad
hoc networks (MANET) is one of the greatest security concerns in
the deployment of wireless networks. Thus communication in
MANET functions properly only if the participating nodes cooperate
in routing without any malicious intention. However, some of the
nodes may be malicious in their behavior, by indulging in flooding
attacks on their neighbors. Some others may act malicious by
launching active security attacks like denial of service. This paper
addresses few related works done on trust evaluation and
establishment in ad hoc networks. Related works on flooding attack
prevention are reviewed. A new trust approach based on the extent of
friendship between the nodes is proposed which makes the nodes to
co-operate and prevent flooding attacks in an ad hoc environment.
The performance of the trust algorithm is tested in an ad hoc network
implementing the Ad hoc On-demand Distance Vector (AODV)
protocol.
Abstract: The equivalence class subset algorithm is a powerful
tool for solving a wide variety of constraint satisfaction problems and
is based on the use of a decision function which has a very high but
not perfect accuracy. Perfect accuracy is not required in the decision
function as even a suboptimal solution contains valuable information
that can be used to help find an optimal solution. In the hardest
problems, the decision function can break down leading to a
suboptimal solution where there are more equivalence classes than
are necessary and which can be viewed as a mixture of good decision
and bad decisions. By choosing a subset of the decisions made in
reaching a suboptimal solution an iterative technique can lead to an
optimal solution, using series of steadily improved suboptimal
solutions. The goal is to reach an optimal solution as quickly as
possible. Various techniques for choosing the decision subset are
evaluated.
Abstract: A highly optimized implementation of binary mixture
diffusion with no initial bulk velocity on graphics processors is
presented. The lattice Boltzmann model is employed for simulating
the binary diffusion of oxygen and nitrogen into each other with
different initial concentration distributions. Simulations have been
performed using the latest proposed lattice Boltzmann model that
satisfies both the indifferentiability principle and the H-theorem for
multi-component gas mixtures. Contemporary numerical
optimization techniques such as memory alignment and increasing
the multiprocessor occupancy are exploited along with some novel
optimization strategies to enhance the computational performance on
graphics processors using the C for CUDA programming language.
Speedup of more than two orders of magnitude over single-core
processors is achieved on a variety of Graphical Processing Unit
(GPU) devices ranging from conventional graphics cards to
advanced, high-end GPUs, while the numerical results are in
excellent agreement with the available analytical and numerical data
in the literature.
Abstract: The Requirements Abstraction Model (RAM) helps in managing abstraction in requirements by organizing them at four levels (product, feature, function and component). The RAM is adaptable and can be tailored to meet the needs of the various organizations. Because software requirements are an important source of information for developing high-level tests, organizations willing to adopt the RAM model need to know the suitability of the RAM requirements for developing high-level tests. To investigate this suitability, test cases from twenty randomly selected requirements were developed, analyzed and graded. Requirements were selected from the requirements document of a Course Management System, a web based software system that supports teachers and students in performing course related tasks. This paper describes the results of the requirements document analysis. The results show that requirements at lower levels in the RAM are suitable for developing executable tests whereas it is hard to develop from requirements at higher levels.
Abstract: To define or predict incipient motion in an alluvial
channel, most of the investigators use a standard or modified form of
Shields- diagram. Shields- diagram does give a process to determine
the incipient motion parameters but an iterative one. To design
properly (without iteration), one should have another equation for
resistance. Absence of a universal resistance equation also magnifies
the difficulties in defining the model. Neural network technique,
which is particularly useful in modeling a complex processes, is
presented as a tool complimentary to modeling incipient motion.
Present work develops a neural network model employing the RBF
network to predict the average velocity u and water depth y based on
the experimental data on incipient condition. Based on the model,
design curves have been presented for the field application.
Abstract: Next Generation Wireless Network (NGWN) is
expected to be a heterogeneous network which integrates all different
Radio Access Technologies (RATs) through a common platform. A
major challenge is how to allocate users to the most suitable RAT for
them. An optimized solution can lead to maximize the efficient use
of radio resources, achieve better performance for service providers
and provide Quality of Service (QoS) with low costs to users.
Currently, Radio Resource Management (RRM) is implemented
efficiently for the RAT that it was developed. However, it is not
suitable for a heterogeneous network. Common RRM (CRRM) was
proposed to manage radio resource utilization in the heterogeneous
network. This paper presents a user level Markov model for a three
co-located RAT networks. The load-balancing based and service
based CRRM algorithms have been studied using the presented
Markov model. A comparison for the performance of load-balancing
based and service based CRRM algorithms is studied in terms of
traffic distribution, new call blocking probability, vertical handover
(VHO) call dropping probability and throughput.
Abstract: In this paper we report a study aimed at determining
the most effective animation technique for representing ASL
(American Sign Language) finger-spelling. Specifically, in the study
we compare two commonly used 3D computer animation methods
(keyframe animation and motion capture) in order to ascertain which
technique produces the most 'accurate', 'readable', and 'close to
actual signing' (i.e. realistic) rendering of ASL finger-spelling. To
accomplish this goal we have developed 20 animated clips of fingerspelled
words and we have designed an experiment consisting of a
web survey with rating questions. 71 subjects ages 19-45 participated
in the study. Results showed that recognition of the words was
correlated with the method used to animate the signs. In particular,
keyframe technique produced the most accurate representation of the
signs (i.e., participants were more likely to identify the words
correctly in keyframed sequences rather than in motion captured
ones). Further, findings showed that the animation method had an
effect on the reported scores for readability and closeness to actual
signing; the estimated marginal mean readability and closeness was
greater for keyframed signs than for motion captured signs. To our
knowledge, this is the first study aimed at measuring and comparing
accuracy, readability and realism of ASL animations produced with
different techniques.
Abstract: Quasigroups are algebraic structures closely related to
Latin squares which have many different applications. The
construction of block cipher is based on quasigroup string
transformation. This article describes a block cipher based
Quasigroup of order 256, suitable for fast software encryption of
messages written down in universal ASCII code. The novelty of this
cipher lies on the fact that every time the cipher is invoked a new set
of two randomly generated quasigroups are used which in turn is
used to create a pair of quasigroup of dual operations. The
cryptographic strength of the block cipher is examined by calculation
of the xor-distribution tables. In this approach some algebraic
operations allows quasigroups of huge order to be used without any
requisite to be stored.
Abstract: Routing in MANET is extremely challenging because
of MANETs dynamic features, its limited bandwidth, frequent
topology changes caused by node mobility and power energy
consumption. In order to efficiently transmit data to destinations, the
applicable routing algorithms must be implemented in mobile ad-hoc
networks. Thus we can increase the efficiency of the routing by
satisfying the Quality of Service (QoS) parameters by developing
routing algorithms for MANETs. The algorithms that are inspired by
the principles of natural biological evolution and distributed
collective behavior of social colonies have shown excellence in
dealing with complex optimization problems and are becoming more
popular. This paper presents a survey on few meta-heuristic
algorithms and naturally-inspired algorithms.
Abstract: In this paper we present the algorithm which allows
us to have an object tracking close to real time in Full HD videos.
The frame rate (FR) of a video stream is considered to be between
5 and 30 frames per second. The real time track building will be
achieved if the algorithm can follow 5 or more frames per second. The
principle idea is to use fast algorithms when doing preprocessing to
obtain the key points and track them after. The procedure of matching
points during assignment is hardly dependent on the number of points.
Because of this we have to limit pointed number of points using the
most informative of them.
Abstract: The evolution of current modeling specifications gives rise to the problem of generating automated test cases from a variety of application tools. Past endeavours on behavioural testing of UML statecharts have not systematically leveraged the potential of existing graph theory for testing of objects. Therefore there exists a need for a simple, tool-independent, and effective method for automatic test generation. An architecture, codenamed ACUTE-J (Automated stateChart Unit Testing Engine for Java), for automating the unit test generation process is presented. A sequential approach for converting UML statechart diagrams to JUnit test classes is described, with the application of existing graph theory. Research byproducts such as a universal XML Schema and API for statechart-driven testing are also proposed. The result from a Java implementation of ACUTE-J is discussed in brief. The Chinese Postman algorithm is utilised as an illustration for a run-through of the ACUTE-J architecture.