Abstract: Mammalian genomes contain large number of
retroelements (SINEs, LINEs and LTRs) which could affect
expression of protein coding genes through associated transcription
factor binding sites (TFBS). Activity of the retroelement-associated
TFBS in many genes is confirmed experimentally but their global
functional impact remains unclear. Human SINEs (Alu repeats) and
mouse SINEs (B1 and B2 repeats) are known to be clustered in GCrich
gene rich genome segments consistent with the view that they
can contribute to regulation of gene expression. We have shown
earlier that Alu are involved in formation of cis-regulatory modules
(clusters of TFBS) in human promoters, and other authors reported
that Alu located near promoter CpG islands have an increased
frequency of CpG dinucleotides suggesting that these Alu are
undermethylated. Human Alu and mouse B1/B2 elements have an
internal bipartite promoter for RNA polymerase III containing
conserved sequence motif called B-box which can bind basal
transcription complex TFIIIC. It has been recently shown that TFIIIC
binding to B-box leads to formation of a boundary which limits
spread of repressive chromatin modifications in S. pombe. SINEassociated
B-boxes may have similar function but conservation of
TFIIIC binding sites in SINEs located near mammalian promoters
has not been studied earlier. Here we analysed abundance and
distribution of retroelements (SINEs, LINEs and LTRs) in annotated
sequences of the Database of mammalian transcription start sites
(DBTSS). Fractions of SINEs in human and mouse promoters are
slightly lower than in all genome but >40% of human and mouse
promoters contain Alu or B1/B2 elements within -1000 to +200 bp
interval relative to transcription start site (TSS). Most of these SINEs
is associated with distal segments of promoters (-1000 to -200 bp
relative to TSS) indicating that their insertion at distances >200 bp
upstream of TSS is tolerated during evolution. Distribution of SINEs
in promoters correlates negatively with the distribution of CpG
sequences. Using analysis of abundance of 12-mer motifs from the
B1 and Alu consensus sequences in genome and DBTSS it has been
confirmed that some subsegments of Alu and B1 elements are poorly
conserved which depends in part on the presence of CpG
dinucleotides. One of these CpG-containing subsegments in B1
elements overlaps with SINE-associated B-box and it shows better
conservation in DBTSS compared to genomic sequences. It has been
also studied conservation in DBTSS and genome of the B-box
containing segments of old (AluJ, AluS) and young (AluY) Alu
repeats and found that CpG sequence of the B-box of old Alu is
better conserved in DBTSS than in genome. This indicates that Bbox-
associated CpGs in promoters are better protected from
methylation and mutation than B-box-associated CpGs in genomic
SINEs. These results are consistent with the view that potential
TFIIIC binding motifs in SINEs associated with human and mouse
promoters may be functionally important. These motifs may protect
promoters from repressive histone modifications which spread from
adjacent sequences. This can potentially explain well known
clustering of SINEs in GC-rich gene rich genome compartments and
existence of unmethylated CpG islands.
Abstract: The radius-of-curvature (ROC) defines the degree of
curvature along the centerline of a roadway whereby a travelling
vehicle must follow. Roadway designs must encompass ROC in
mitigating the cost of earthwork associated with construction while
also allowing vehicles to travel at maximum allowable design speeds.
Thus, a road will tend to follow natural topography where possible,
but curvature must also be optimized to permit fast, but safe vehicle
speeds. The more severe the curvature of the road, the slower the
permissible vehicle speed. For route planning, whether for urban
settings, emergency operations, or even parcel delivery, ROC is a
necessary attribute of road arcs for computing travel time.
It is extremely rare for a geo-spatial database to contain ROC. This
paper will present a procedure and mathematical algorithm to
calculate and assign ROC to a segment pair and/or polyline.
Abstract: This paper presents an algorithm based on the
wavelet decomposition, for feature extraction from the ECG signal
and recognition of three types of Ventricular Arrhythmias using
neural networks. A set of Discrete Wavelet Transform (DWT)
coefficients, which contain the maximum information about the
arrhythmias, is selected from the wavelet decomposition. After that a
novel clustering algorithm based on nature inspired algorithm (Ant
Colony Optimization) is developed for classifying arrhythmia types.
The algorithm is applied on the ECG registrations from the MIT-BIH
arrhythmia and malignant ventricular arrhythmia databases. We
applied Daubechies 4 wavelet in our algorithm. The wavelet
decomposition enabled us to perform the task efficiently and
produced reliable results.
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 will present the implementation of QoS
policy based system by utilizing rules on Access Control List (ACL)
over Layer 3 (L3) switch. Also presented is the architecture on that
implementation; the tools being used and the result were gathered.
The system architecture has an ability to control ACL rules which are
installed inside an external L3 switch. ACL rules used to instruct the
way of access control being executed, in order to entertain all traffics
through that particular switch. The main advantage of using this
approach is that the single point of failure could be prevented when
there are any changes on ACL rules inside L3 switches. Another
advantage is that the agent could instruct ACL rules automatically
straight away based on the changes occur on policy database without
configuring them one by one. Other than that, when QoS policy
based system was implemented in distributed environment, the
monitoring process can be synchronized easily due to the automate
process running by agent over external policy devices.
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: 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: 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 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: At present, dictionary attack has been the basic tool for
recovering key passwords. In order to avoid dictionary attack, users
purposely choose another character strings as passwords. According to
statistics, about 14% of users choose keys on a keyboard (Kkey, for
short) as passwords. This paper develops a framework system to attack
the password chosen from Kkeys and analyzes its efficiency. Within
this system, we build up keyboard rules using the adjacent and parallel
relationship among Kkeys and then use these Kkey rules to generate
password databases by depth-first search method. According to the
experiment results, we find the key space of databases derived from
these Kkey rules that could be far smaller than the password databases
generated within brute-force attack, thus effectively narrowing down
the scope of attack research. Taking one general Kkey rule, the
combinations in all printable characters (94 types) with Kkey adjacent
and parallel relationship, as an example, the derived key space is about
240 smaller than those in brute-force attack. In addition, we
demonstrate the method's practicality and value by successfully
cracking the access password to UNIX and PC using the password
databases created
Abstract: The various types of frequent pattern discovery
problem, namely, the frequent itemset, sequence and graph mining
problems are solved in different ways which are, however, in certain
aspects similar. The main approach of discovering such patterns can
be classified into two main classes, namely, in the class of the levelwise
methods and in that of the database projection-based methods.
The level-wise algorithms use in general clever indexing structures
for discovering the patterns. In this paper a new approach is proposed
for discovering frequent sequences and tree-like patterns efficiently
that is based on the level-wise issue. Because the level-wise
algorithms spend a lot of time for the subpattern testing problem, the
new approach introduces the idea of using automaton theory to solve
this problem.
Abstract: In this study, an inland metropolitan area, Gwangju, in Korea was selected to assess the amplification potential of earthquake motion and provide the information for regional seismic countermeasure. A geographic information system-based expert system was implemented for reliably predicting the spatial geotechnical layers in the entire region of interesting by building a geo-knowledge database. Particularly, the database consists of the existing boring data gathered from the prior geotechnical projects and the surface geo-knowledge data acquired from the site visit. For practical application of the geo-knowledge database to estimate the earthquake hazard potential related to site amplification effects at the study area, seismic zoning maps on geotechnical parameters, such as the bedrock depth and the site period, were created within GIS framework. In addition, seismic zonation of site classification was also performed to determine the site amplification coefficients for seismic design at any site in the study area. KeywordsEarthquake hazard, geo-knowledge, geographic information system, seismic zonation, site period.
Abstract: Abovepresented work deals with the new scope of application of information and communication technologies for the improvement of the election process in the biased environment. We are introducing a new concept of construction of the information-communication system for the election participant. It consists of four main components: Software, Physical Infrastructure, Structured Information and the Trained Stuff. The Structured Information is the bases of the whole system and is the collection of all possible events (irregularities among them) at the polling stations, which are structured in special templates, forms and integrated in mobile devices.The software represents a package of analytic modules, which operates with the dynamic database. The application of modern communication technologies facilities the immediate exchange of information and of relevant documents between the polling stations and the Server of the participant. No less important is the training of the staff for the proper functioning of the system. The e-training system with various modules should be applied in this respect. The presented methodology is primarily focused on the election processes in the countries of emerging democracies.It can be regarded as the tool for the monitoring of elections process by the political organization(s) and as one of the instruments to foster the spread of democracy in these countries.
Abstract: the data of Taiwanese 8th grader in the 4th cycle of
Trends in International Mathematics and Science Study (TIMSS) are
analyzed to examine the influence of the science teachers- preference
in experimental teaching on the relationships between the affective
variables ( the perceived usefulness of science, ease of using science
and science learning interest) and the academic achievement in science.
After dealing with the missing data, 3711 students and 145 science
teacher-s data were analyzed through a Hierarchical Linear Modeling
technique. The major objective of this study was to determine the role
of the experimental teaching moderates the relationship between
perceived usefulness and achievement.
Abstract: A lot of research has been done in the past decade in the field of audio content analysis for extracting various information from audio signal. One such significant information is the "perceived mood" or the "emotions" related to a music or audio clip. This information is extremely useful in applications like creating or adapting the play-list based on the mood of the listener. This information could also be helpful in better classification of the music database. In this paper we have presented a method to classify music not just based on the meta-data of the audio clip but also include the "mood" factor to help improve the music classification. We propose an automated and efficient way of classifying music samples based on the mood detection from the audio data. We in particular try to classify the music based on mood for Indian bollywood music. The proposed method tries to address the following problem statement: Genre information (usually part of the audio meta-data) alone does not help in better music classification. For example the acoustic version of the song "nothing else matters by Metallica" can be classified as melody music and thereby a person in relaxing or chill out mood might want to listen to this track. But more often than not this track is associated with metal / heavy rock genre and if a listener classified his play-list based on the genre information alone for his current mood, the user shall miss out on listening to this track. Currently methods exist to detect mood in western or similar kind of music. Our paper tries to solve the issue for Indian bollywood music from an Indian cultural context
Abstract: Adaptive e-learning today gives the student a central
role in his own learning process. It allows learners to try things out,
participate in courses like never before, and get more out of learning
than before. In this paper, an adaptive e-learning model for logic
design, simplification of Boolean functions and related fields is
presented. Such model presents suitable courses for each student in a
dynamic and adaptive manner using existing database and workflow
technologies. The main objective of this research work is to provide
an adaptive e-learning model based learners' personality using
explicit and implicit feedback. To recognize the learner-s, we develop
dimensions to decide each individual learning style in order to
accommodate different abilities of the users and to develop vital
skills. Thus, the proposed model becomes more powerful, user
friendly and easy to use and interpret. Finally, it suggests a learning
strategy and appropriate electronic media that match the learner-s
preference.
Abstract: Iris-based biometric system is gaining its importance in several applications. However, processing of iris biometric is a challenging and time consuming task. Detection of iris part in an eye image poses a number of challenges such as, inferior image quality, occlusion of eyelids and eyelashes etc. Due to these problems it is not possible to achieve 100% accuracy rate in any iris-based biometric authentication systems. Further, iris detection is a computationally intensive task in the overall iris biometric processing. In this paper, we address these two problems and propose a technique to localize iris part efficiently and accurately. We propose scaling and color level transform followed by thresholding, finding pupil boundary points for pupil boundary detection and dilation, thresholding, vertical edge detection and removal of unnecessary edges present in the eye images for iris boundary detection. Scaling reduces the search space significantly and intensity level transform is helpful for image thresholding. Experimental results show that our approach is comparable with the existing approaches. Following our approach it is possible to detect iris part with 95-99% accuracy as substantiated by our experiments on CASIA Ver-3.0, ICE 2005, UBIRIS, Bath and MMU iris image databases.
Abstract: In this paper, our concern is the management of mobile transactions in the shared area among many servers, when the mobile user moves from one cell to another in online partiallyreplicated distributed mobile database environment. We defined the concept of transaction and classified the different types of transactions. Based on this analysis, we propose an algorithm that handles the disconnection due to moving among sites.
Abstract: This paper describes the use of artificial neural
networks (ANN) for predicting non-linear layer moduli of flexible
airfield pavements subjected to new generation aircraft (NGA)
loading, based on the deflection profiles obtained from Heavy
Weight Deflectometer (HWD) test data. The HWD test is one of the
most widely used tests for routinely assessing the structural integrity
of airport pavements in a non-destructive manner. The elastic moduli
of the individual pavement layers backcalculated from the HWD
deflection profiles are effective indicators of layer condition and are
used for estimating the pavement remaining life. HWD tests were
periodically conducted at the Federal Aviation Administration-s
(FAA-s) National Airport Pavement Test Facility (NAPTF) to
monitor the effect of Boeing 777 (B777) and Beoing 747 (B747) test
gear trafficking on the structural condition of flexible pavement
sections. In this study, a multi-layer, feed-forward network which
uses an error-backpropagation algorithm was trained to approximate
the HWD backcalculation function. The synthetic database generated
using an advanced non-linear pavement finite-element program was
used to train the ANN to overcome the limitations associated with
conventional pavement moduli backcalculation. The changes in
ANN-based backcalculated pavement moduli with trafficking were
used to compare the relative severity effects of the aircraft landing
gears on the NAPTF test pavements.
Abstract: For a spatiotemporal database management system,
I/O cost of queries and other operations is an important performance
criterion. In order to optimize this cost, an intense research on
designing robust index structures has been done in the past decade.
With these major considerations, there are still other design issues
that deserve addressing due to their direct impact on the I/O cost.
Having said this, an efficient buffer management strategy plays a key
role on reducing redundant disk access. In this paper, we proposed an
efficient buffer strategy for a spatiotemporal database index
structure, specifically indexing objects moving over a network of
roads. The proposed strategy, namely MONPAR, is based on the data
type (i.e. spatiotemporal data) and the structure of the index
structure. For the purpose of an experimental evaluation, we set up a
simulation environment that counts the number of disk accesses
while executing a number of spatiotemporal range-queries over the
index. We reiterated simulations with query sets with different
distributions, such as uniform query distribution and skewed query
distribution. Based on the comparison of our strategy with wellknown
page-replacement techniques, like LRU-based and Prioritybased
buffers, we conclude that MONPAR behaves better than its
competitors for small and medium size buffers under all used query-distributions.