Abstract: The emergence of the Internet has brewed the
revolution of information storage and retrieval. As most of the
data in the web is unstructured, and contains a mix of text,
video, audio etc, there is a need to mine information to cater to
the specific needs of the users without loss of important
hidden information. Thus developing user friendly and
automated tools for providing relevant information quickly
becomes a major challenge in web mining research. Most of
the existing web mining algorithms have concentrated on
finding frequent patterns while neglecting the less frequent
ones that are likely to contain outlying data such as noise,
irrelevant and redundant data. This paper mainly focuses on
Signed approach and full word matching on the organized
domain dictionary for mining web content outliers. This
Signed approach gives the relevant web documents as well as
outlying web documents. As the dictionary is organized based
on the number of characters in a word, searching and retrieval
of documents takes less time and less space.
Abstract: In order to consider the effects of the higher modes in
the pushover analysis, during the recent years several multi-modal
pushover procedures have been presented. In these methods the
response of the considered modes are combined by the square-rootof-
sum-of-squares (SRSS) rule while application of the elastic modal
combination rules in the inelastic phases is no longer valid. In this
research the feasibility of defining an efficient alternative
combination method is investigated. Two steel moment-frame
buildings denoted SAC-9 and SAC-20 under ten earthquake records
are considered. The nonlinear responses of the structures are
estimated by the directed algebraic combination of the weighted
responses of the separate modes. The weight of the each mode is
defined so that the resulted response of the combination has a
minimum error to the nonlinear time history analysis. The genetic
algorithm (GA) is used to minimize the error and optimize the weight
factors. The obtained optimal factors for each mode in different cases
are compared together to find unique appropriate weight factors for
each mode in all cases.
Abstract: In this paper we introduce a new unit test technique
called déjà-vu object. Déjà-vu objects replace real objects used by
classes under test, allowing the execution of isolated unit tests. A
déjà-vu object is able to observe and record the behaviour of a real
object during real sessions, and to replace it during unit tests,
returning previously recorded results. Consequently déjà-vu object
technique can be useful when a bottom-up development and testing
strategy is adopted. In this case déjà-vu objects can increase test
portability and test source code readability. At the same time they
can reduce the time spent by programmers to develop test code and
the risk of incompatibility during the switching between déjà-vu and
production code.
Abstract: Due to its special data structure and manipulative principle, Object-Oriented Database (OODB) has a particular security protection and authorization methods. This paper first introduces the features of security mechanism about OODB, and then talked about authorization checking process of OODB. Implicit authorization mechanism is based on the subject hierarchies, object hierarchies and access hierarchies of the security authorization modes, and simplifies the authorization mode. In addition, to combine with other authorization mechanisms, implicit authorization can make protection on the authorization of OODB expediently and effectively.
Abstract: New software protection product called “Obfuscation
Studio" is presented in the paper. Several obfuscating modules that
are already implemented are described. Some theoretical data is
presented, that shows the potency and effectiveness of described
obfuscation methods. “Obfuscation Studio" is being implemented for
protecting programs written for .NET platform, but the described
methods can also be interesting for other applications.
Abstract: DNA microarray technology is widely used by
geneticists to diagnose or treat diseases through gene expression.
This technology is based on the hybridization of a tissue-s DNA
sequence into a substrate and the further analysis of the image
formed by the thousands of genes in the DNA as green, red or yellow
spots. The process of DNA microarray image analysis involves
finding the location of the spots and the quantification of the
expression level of these. In this paper, a tool to perform DNA
microarray image analysis is presented, including a spot addressing
method based on the image projections, the spot segmentation
through contour based segmentation and the extraction of relevant
information due to gene expression.
Abstract: In this paper, a near lossless image coding scheme
based on Orthogonal Polynomials Transform (OPT) has been
presented. The polynomial operators and polynomials basis operators
are obtained from set of orthogonal polynomials functions for the
proposed transform coding. The image is partitioned into a number of
distinct square blocks and the proposed transform coding is applied to
each of these individually. After applying the proposed transform
coding, the transformed coefficients are rearranged into a sub-band
structure. The Embedded Zerotree (EZ) coding algorithm is then
employed to quantize the coefficients. The proposed transform is
implemented for various block sizes and the performance is
compared with existing Discrete Cosine Transform (DCT) transform
coding scheme.
Abstract: An ontology is a data model that represents a set of
concepts in a given field and the relationships among those concepts.
As the emphasis on achieving a semantic web continues to escalate,
ontologies for all types of domains increasingly will be developed.
These ontologies may become large and complex, and as their size
and complexity grows, so will the need for multi-user interfaces for
ontology curation. Herein a functionally comprehensive, generic
approach to maintaining an ontology as a relational database is
presented. Unlike many other ontology editors that utilize a database,
this approach is entirely domain-generic and fully supports Webbased,
collaborative editing including the designation of different
levels of authorization for users.
Abstract: Investigating language acquisition is one of the most
challenging problems in the area of studying language. Syllable
learning as a level of language acquisition has a considerable
significance since it plays an important role in language acquisition.
Because of impossibility of studying language acquisition directly
with children, especially in its developmental phases, computer
models will be useful in examining language acquisition. In this
paper a computer model of early language learning for syllable
learning is proposed. It is guided by a conceptual model of syllable
learning which is named Directions Into Velocities of Articulators
model (DIVA). The computer model uses simple associational and
reinforcement learning rules within neural network architecture
which are inspired by neuroscience. Our simulation results verify the
ability of the proposed computer model in producing phonemes
during babbling and early speech. Also, it provides a framework for
examining the neural basis of language learning and communication
disorders.
Abstract: In this paper, we proposed the robust mobile object
detection method for light effect in the night street image block based
updating reference background model using block state analysis.
Experiment image is acquired sequence color video from steady
camera. When suddenly appeared artificial illumination, reference
background model update this information such as street light, sign
light. Generally natural illumination is change by temporal, but
artificial illumination is suddenly appearance. So in this paper for
exactly detect artificial illumination have 2 state process. First process
is compare difference between current image and reference
background by block based, it can know changed blocks. Second
process is difference between current image-s edge map and reference
background image-s edge map, it possible to estimate illumination at
any block. This information is possible to exactly detect object,
artificial illumination and it was generating reference background
more clearly. Block is classified by block-state analysis. Block-state
has a 4 state (i.e. transient, stationary, background, artificial
illumination). Fig. 1 is show characteristic of block-state respectively
[1]. Experimental results show that the presented approach works well
in the presence of illumination variance.
Abstract: In this work we propose a novel Steganographic
method for hiding information within the spatial domain of the gray
scale image. The proposed approach works by dividing the cover into
blocks of equal sizes and then embeds the message in the edge of the
block depending on the number of ones in left four bits of the pixel.
The proposed approach is tested on a database consists of 100
different images. Experimental results, compared with other
methods, showed that the proposed approach hide more large
information and gave a good visual quality stego-image that can be
seen by human eyes.
Abstract: In this paper we focus on event extraction from Tamil
news article. This system utilizes a scoring scheme for extracting and
grouping event-specific sentences. Using this scoring scheme eventspecific
clustering is performed for multiple documents. Events are
extracted from each document using a scoring scheme based on
feature score and condition score. Similarly event specific sentences
are clustered from multiple documents using this scoring scheme.
The proposed system builds the Event Template based on user
specified query. The templates are filled with event specific details
like person, location and timeline extracted from the formed clusters.
The proposed system applies these methodologies for Tamil news
articles that have been enconverted into UNL graphs using a Tamil to
UNL-enconverter. The main intention of this work is to generate an
event based template.
Abstract: The paper aims to specify and build a system, a learning support in radiology-senology (breast radiology) dedicated to help assist junior radiologists-senologists in their radiologysenology- related activity based on experience of expert radiologistssenologists. This system is named SAFRS (i.e. system supporting the training of radiologists-senologists). It is based on the exploitation of radiologic-senologic images (primarily mammograms but also echographic images or MRI) and their related clinical files. The aim of such a system is to help breast cancer screening in education. In order to acquire this expert radiologist-senologist knowledge, we have used the CBR (case-based reasoning) approach. The SAFRS system will promote the evolution of teaching in radiology-senology by offering the “junior radiologist" trainees an advanced pedagogical product. It will permit a strengthening of knowledge together with a very elaborate presentation of results. At last, the know-how will derive from all these factors.
Abstract: In this paper a new Joint Adaptive Block Matching
Search (JABMS) algorithm is proposed to generate motion vector
and search a best match macro block by classifying the motion vector
movement based on prediction error. Diamond Search (DS)
algorithm generates high estimation accuracy when motion vector is
small and Adaptive Rood Pattern Search (ARPS) algorithm can
handle large motion vector but is not very accurate. The proposed
JABMS algorithm which is capable of considering both small and
large motions gives improved estimation accuracy and the
computational cost is reduced by 15.2 times compared with
Exhaustive Search (ES) algorithm and is 1.3 times less compared
with Diamond search algorithm.
Abstract: Outlier detection in streaming data is very challenging because streaming data cannot be scanned multiple times and also new concepts may keep evolving. Irrelevant attributes can be termed as noisy attributes and such attributes further magnify the challenge of working with data streams. In this paper, we propose an unsupervised outlier detection scheme for streaming data. This scheme is based on clustering as clustering is an unsupervised data mining task and it does not require labeled data, both density based and partitioning clustering are combined for outlier detection. In this scheme partitioning clustering is also used to assign weights to attributes depending upon their respective relevance and weights are adaptive. Weighted attributes are helpful to reduce or remove the effect of noisy attributes. Keeping in view the challenges of streaming data, the proposed scheme is incremental and adaptive to concept evolution. Experimental results on synthetic and real world data sets show that our proposed approach outperforms other existing approach (CORM) in terms of outlier detection rate, false alarm rate, and increasing percentages of outliers.
Abstract: This paper proposes a Wavelength Division
Multiplexing (WDM) technology based Storage Area Network
(SAN) for all type of Disaster recovery operation. It considers
recovery when all paths failure in the network as well as the main
SAN site failure also the all backup sites failure by the effect of
natural disasters such as earthquakes, fires and floods, power outage,
and terrorist attacks, as initially SAN were designed to work within
distance limited environments[2]. Paper also presents a NEW PATH
algorithm when path failure occurs. The simulation result and
analysis is presented for the proposed architecture with performance
consideration.
Abstract: The approach based on the wavelet transform has
been widely used for image denoising due to its multi-resolution
nature, its ability to produce high levels of noise reduction and the
low level of distortion introduced. However, by removing noise, high
frequency components belonging to edges are also removed, which
leads to blurring the signal features. This paper proposes a new
method of image noise reduction based on local variance and edge
analysis. The analysis is performed by dividing an image into 32 x 32
pixel blocks, and transforming the data into wavelet domain. Fast
lifting wavelet spatial-frequency decomposition and reconstruction is
developed with the advantages of being computationally efficient and
boundary effects minimized. The adaptive thresholding by local
variance estimation and edge strength measurement can effectively
reduce image noise while preserve the features of the original image
corresponding to the boundaries of the objects. Experimental results
demonstrate that the method performs well for images contaminated
by natural and artificial noise, and is suitable to be adapted for
different class of images and type of noises. The proposed algorithm
provides a potential solution with parallel computation for real time
or embedded system application.
Abstract: In this paper, we deal with the Steiner tree problem
(STP) on a graph in which a fuzzy number, instead of a real number,
is assigned to each edge. We propose a modification of the shortest
paths approximation based on the fuzzy shortest paths (FSP)
evaluations. Since a fuzzy min operation using the extension
principle leads to nondominated solutions, we propose another
approach to solving the FSP using Cheng's centroid point fuzzy
ranking method.
Abstract: An accident is an unexpected and unplanned situation
that happens and affects human in a negative outcome. The accident
can cause an injury to a human biological organism. Thus, the
provision of initial care for an illness or injury is very important
move to prepare the patients/victims before sending to the doctor. In
this paper, a First Aid Application is developed to give some
directions for preliminary taking care of patient/victim via Android
mobile device. Also, the navigation function using Google Maps API
is implemented in this paper for searching a suitable path to the
nearest hospital. Therefore, in the emergency case, this function can
be activated and navigate patients/victims to the hospital with the
shortest path.
Abstract: Route bus system is the fundamental public transportation
system and has an important role in every province. To improve
the usability of it greatly, we develop an AR application for "Bus-
Net". The Bus-Net system is the shortest path planning system.
Bus-Net supports bus users to make a plan to change buses by
providing them with information about the direction. However, with
Bus-Net, these information are provided in text-base. It is difficult
to understand them for the person who does not know the place. We
developed the AR application for Bus-Net. It supports the action of
a bus user in an innovative way by putting information on a camera
picture and leading the way to a bus stop. The application also inform
the user the correct bus to get, the direction the bus takes and the
fare, which ease many anxieties and worries people tend to feel when
they take buses.