Abstract: Most of researches for conventional simulations were
studied focusing on flocks with a single species. While there exist the
flocking behaviors with a single species in nature, the flocking
behaviors are frequently observed with multi-species. This paper
studies on the flocking simulation for heterogeneous agents. In order
to simulate the flocks for heterogeneous agents, the conventional
method uses the identifier of flock, while the proposed method defines
the feature vector of agent and uses the similarity between agents by
comparing with those feature vectors. Based on the similarity, the
paper proposed the attractive force and repulsive force and then
executed the simulation by applying two forces. The results of
simulation showed that flock formation with heterogeneous agents is
very natural in both cases. In addition, it showed that unlike the
existing method, the proposed method can not only control the density
of the flocks, but also be possible for two different groups of agents to
flock close to each other if they have a high similarity.
Abstract: The prediction of meteorological parameters at a
meteorological station is an interesting and open problem. A firstorder
linear dynamic model GM(1,1) is the main component of the
grey system theory. The grey model requires only a few previous data
points in order to make a real-time forecast. In this paper, we
consider the daily average ambient temperature as a time series and
the grey model GM(1,1) applied to local prediction (short-term
prediction) of the temperature. In the same case study we use a fuzzy
predictive model for global prediction. We conclude the paper with a
comparison between local and global prediction schemes.
Abstract: HIV-1 genome is highly heterogeneous. Due to this
variation, features of HIV-I genome is in a wide range. For this
reason, the ability to infection of the virus changes depending on
different chemokine receptors. From this point of view, R5 HIV
viruses use CCR5 coreceptor while X4 viruses use CXCR5 and
R5X4 viruses can utilize both coreceptors. Recently, in
Bioinformatics, R5X4 viruses have been studied to classify by using
the experiments on HIV-1 genome.
In this study, R5X4 type of HIV viruses were classified using
Auto Regressive (AR) model through Artificial Neural Networks
(ANNs). The statistical data of R5X4, R5 and X4 viruses was
analyzed by using signal processing methods and ANNs. Accessible
residues of these virus sequences were obtained and modeled by AR
model since the dimension of residues is large and different from
each other. Finally the pre-processed data was used to evolve various
ANN structures for determining R5X4 viruses. Furthermore ROC
analysis was applied to ANNs to show their real performances. The
results indicate that R5X4 viruses successfully classified with high
sensitivity and specificity values training and testing ROC analysis
for RBF, which gives the best performance among ANN structures.
Abstract: In data mining, the association rules are used to search
for the relations of items of the transactions database. Following the
data is collected and stored, it can find rules of value through
association rules, and assist manager to proceed marketing strategy
and plan market framework. In this paper, we attempt fuzzy partition
methods and decide membership function of quantitative values of
each transaction item. Also, by managers we can reflect the
importance of items as linguistic terms, which are transformed as
fuzzy sets of weights. Next, fuzzy weighted frequent pattern growth
(FWFP-Growth) is used to complete the process of data mining. The
method above is expected to improve Apriori algorithm for its better
efficiency of the whole association rules. An example is given to
clearly illustrate the proposed approach.
Abstract: Many problems in computer vision and image
processing present potential for parallel implementations through one
of the three major paradigms of geometric parallelism, algorithmic
parallelism and processor farming. Static process scheduling
techniques are used successfully to exploit geometric and algorithmic
parallelism, while dynamic process scheduling is better suited to
dealing with the independent processes inherent in the process
farming paradigm. This paper considers the application of parallel or
multi-computers to a class of problems exhibiting spatial data
characteristic of the geometric paradigm. However, by using
processor farming paradigm, a dynamic scheduling technique is
developed to suit the MIMD structure of the multi-computers. A
hybrid scheme of scheduling is also developed and compared with
the other schemes. The specific problem chosen for the investigation
is the Hough transform for line detection.
Abstract: The novelty proposed in this study is twofold and consists in the developing of a new color similarity metric based on the human visual system and a new color indexing based on a textual approach. The new color similarity metric proposed is based on the color perception of the human visual system. Consequently the results returned by the indexing system can fulfill as much as possibile the user expectations. We developed a web application to collect the users judgments about the similarities between colors, whose results are used to estimate the metric proposed in this study. In order to index the image's colors, we used a text indexing engine to facilitate the integration of visual features in a database of text documents. The textual signature is build by weighting the image's colors in according to their occurrence in the image. The use of a textual indexing engine, provide us a simple, fast and robust solution to index images. A typical usage of the system proposed in this study, is the development of applications whose data type is both visual and textual. In order to evaluate the proposed method we chose a price comparison engine as a case of study, collecting a series of commercial offers containing the textual description and the image representing a specific commercial offer.
Abstract: In general, reports are a form of representing data in
such way that user gets the information he needs. They can be built in
various ways, from the simplest (“select from") to the most complex
ones (results derived from different sources/tables with complex
formulas applied). Furthermore, rules of calculations could be written
as a program hard code or built in the database to be used by dynamic
code. This paper will introduce two types of reports, defined in the
DB structure. The main goal is to manage calculations in optimal
way, keeping maintenance of reports as simple and smooth as
possible.
Abstract: In the upgrade process of enterprise information
systems, how to deal with and utilize those legacy systems affects the
efficiency of construction and development of the new system. We
propose an evaluation system, which comprehensively describes the
capacity of legacy information systems in five aspects. Then we
propose a practical legacy systems evaluation method. Base on the
evaluation result, we can determine the current state of legacy system
which was evaluated.
Abstract: The overall service performance of I/O intensive
system depends mainly on workload on its storage system. In
heterogeneous storage environment where storage elements from
different vendors with different capacity and performance are put
together, workload should be distributed according to storage
capability. This paper addresses data placement issue in short video
sharing website. Workload contributed by a video is estimated by the
number of views and life time span of existing videos in same
category. Experiment was conducted on 42,000 video titles in six
weeks. Result showed that the proposed algorithm distributed
workload and maintained balance better than round robin and random
algorithms.
Abstract: Electronic banking must be secure and easy to use and
many banks heavily advertise an apparent of 100% secure system
which is contestable in many points. In this work, an alternative
approach to the design of e-banking system, through a new solution
for user authentication and security with digital certificate called
LumaCert is introduced. The certificate applies new algorithm for
asymmetric encryption by utilizing two mathematical operators
called Pentors and UltraPentors. The public and private key in this
algorithm represent a quadruple of parameters which are directly
dependent from the above mentioned operators. The strength of the
algorithm resides in the inability to find the respective Pentor and
UltraPentor operator from the mentioned parameters.
Abstract: The most important property of the Gene Ontology is
the terms. These control vocabularies are defined to provide
consistent descriptions of gene products that are shareable and
computationally accessible by humans, software agent, or other
machine-readable meta-data. Each term is associated with
information such as definition, synonyms, database references, amino
acid sequences, and relationships to other terms. This information has
made the Gene Ontology broadly applied in microarray and
proteomic analysis. However, the process of searching the terms is
still carried out using traditional approach which is based on keyword
matching. The weaknesses of this approach are: ignoring semantic
relationships between terms, and highly depending on a specialist to
find similar terms. Therefore, this study combines semantic similarity
measure and genetic algorithm to perform a better retrieval process
for searching semantically similar terms. The semantic similarity
measure is used to compute similitude strength between two terms.
Then, the genetic algorithm is employed to perform batch retrievals
and to handle the situation of the large search space of the Gene
Ontology graph. The computational results are presented to show the
effectiveness of the proposed algorithm.
Abstract: Peer-to-Peer (P2P) is a self-organizing resource sharing network with no centralized authority or infrastructure, which makes it unpredictable and vulnerable. In this paper, we propose architecture to make the peer-to-peer network more centralized, predictable, and safer to use by implementing trust and stopping free riding.
Abstract: Nowadays the devices of night vision are widely used both for military and civil applications. The variety of night vision applications require a variety of the night vision devices designs. A web-based architecture of a software system for design assessment before producing of night vision devices is developed. The proposed architecture of the web-based system is based on the application of a mathematical model for designing of night vision devices. An algorithm with two components – for iterative design and for intelligent design is developed and integrated into system architecture. The iterative component suggests compatible modules combinations to choose from. The intelligent component provides compatible combinations of modules satisfying given user requirements to device parameters. The proposed web-based architecture of a system for design assessment of night vision devices is tested via a prototype of the system. The testing showed the applicability of both iterative and intelligent components of algorithm.
Abstract: Learning using labeled and unlabelled data has
received considerable amount of attention in the machine learning
community due its potential in reducing the need for expensive
labeled data. In this work we present a new method for combining
labeled and unlabeled data based on classifier ensembles. The model
we propose assumes each classifier in the ensemble observes the
input using different set of features. Classifiers are initially trained
using some labeled samples. The trained classifiers learn further
through labeling the unknown patterns using a teaching signals that is
generated using the decision of the classifier ensemble, i.e. the
classifiers self-supervise each other. Experiments on a set of object
images are presented. Our experiments investigate different classifier
models, different fusing techniques, different training sizes and
different input features. Experimental results reveal that the proposed
self-supervised ensemble learning approach reduces classification
error over the single classifier and the traditional ensemble classifier
approachs.
Abstract: In today-s world, the efficient utilization of wood
resources comes more and more to the mind of forest owners. It is a
very complex challenge to ensure an efficient harvest of the wood
resources. This is one of the scopes the project “Virtual Forest II"
addresses. Its core is a database with data about forests containing
approximately 260 million trees located in North Rhine-Westphalia
(NRW). Based on this data, tree growth simulations and wood
mobilization simulations can be conducted. This paper focuses on the
latter. It describes a discrete-event-simulation with an attached 3-D
real time visualization which simulates timber harvest using trees
from the database with different crop resources. This simulation can
be displayed in 3-D to show the progress of the wood crop. All the
data gathered during the simulation is presented as a detailed
summary afterwards. This summary includes cost-benefit
calculations and can be compared to those of previous runs to
optimize the financial outcome of the timber harvest by exchanging
crop resources or modifying their parameters.
Abstract: This paper considers the benefits gained by using an
efficient quality of service management such as DiffServ technique to
improve the performance of military communications. Low delay and
no blockage must be achieved especially for real time tactical data.
All traffic flows generated by different applications do not need same
bandwidth, same latency, same error ratio and this scalable technique
of packet management based on priority levels is analysed. End to
end architectures supporting various traffic flows and including lowbandwidth
and high-delay HF or SHF military links as well as
unprotected Internet sub domains are studied. A tuning of Diffserv
parameters is proposed in accordance with different loads of various
traffic and different operational situations.
Abstract: A computer cluster is a group of tightly coupled
computers that work together closely so that in many respects they
can be viewed as though they are a single computer. The components
of a cluster are commonly, but not always, connected to each other
through fast local area networks. Clusters are usually deployed to
improve performance and/or availability over that provided by a
single computer, while typically being much more cost-effective than
single computers of comparable speed or availability. This paper
proposed the way to implement the Beowulf Cluster in order to
achieve high performance as well as high availability.
Abstract: Due to the constant increase in the volume of information available to applications in fields varying from medical diagnosis to web search engines, accurate support of similarity becomes an important task. This is also the case of spam filtering techniques where the similarities between the known and incoming messages are the fundaments of making the spam/not spam decision. We present a novel approach to filtering based solely on layout, whose goal is not only to correctly identify spam, but also warn about major emerging threats. We propose a mathematical formulation of the email message layout and based on it we elaborate an algorithm to separate different types of emails and find the new, numerically relevant spam types.
Abstract: OLAP uses multidimensional structures, to provide
access to data for analysis. Traditionally, OLAP operations are more
focused on retrieving data from a single data mart. An exception is
the drill across operator. This, however, is restricted to retrieving
facts on common dimensions of the multiple data marts. Our concern
is to define further operations while retrieving data from multiple
data marts. Towards this, we have defined six operations which
coalesce data marts. While doing so we consider the common as well
as the non-common dimensions of the data marts.
Abstract: A robust still image face localization algorithm
capable of operating in an unconstrained visual environment is
proposed. First, construction of a robust skin classifier within a
shifted HSV color space is described. Then various filtering
operations are performed to better isolate face candidates and
mitigate the effect of substantial non-skin regions. Finally, a novel
Bhattacharyya-based face detection algorithm is used to compare
candidate regions of interest with a unique illumination-dependent
face model probability distribution function approximation.
Experimental results show a 90% face detection success rate despite
the demands of the visually noisy environment.