Abstract: Rapid progress in audio compression technology has contributed to the explosive growth of music available in digital form today. In a reversal of ideas, this work makes use of a recently proposed efficient audio compression scheme to develop three important applications in the context of Music Information Retrieval (MIR) for the effective manipulation of large music databases, namely automatic music recommendation (AMR), digital rights management (DRM) and audio finger-printing for song identification. The performance of these three applications has been evaluated with respect to a database of songs collected from a diverse set of genres.
Abstract: In practice, we often come across situations where it is
necessary to make decisions based on incomplete or uncertain data.
In control systems it may be due to the unknown exact mathematical
model, or its excessive complexity (e.g. nonlinearity) when it is
necessary to simplify it, respectively, to solve it using a rule base. In
the case of databases, searching data we compare a similarity
measure with of the requirements of the selection with stored data,
where both the select query and the data itself may contain vague
terms, for example in the form of linguistic qualifiers. In this paper,
we focus on the processing of uncertain data in databases and
demonstrate it on the example multi-criteria decision making in the
selection of variants, specified by higher number of technical
parameters.
Abstract: In this paper, we present a cost-effective wireless
distributed load shedding system for non-emergency scenarios. In
power transformer locations where SCADA system cannot be used,
the proposed solution provides a reasonable alternative that combines
the use of microcontrollers and existing GSM infrastructure to send
early warning SMS messages to users advising them to proactively
reduce their power consumption before system capacity is reached
and systematic power shutdown takes place.
A novel communication protocol and message set have been
devised to handle the messaging between the transformer sites, where
the microcontrollers are located and where the measurements take
place, and the central processing site where the database server is
hosted. Moreover, the system sends warning messages to the endusers
mobile devices that are used as communication terminals. The
system has been implemented and tested via different experimental
results.
Abstract: Recently, lots of researchers are attracted to retrieving
multimedia database by using some impression words and their values.
Ikezoe-s research is one of the representatives and uses eight pairs of
opposite impression words. We had modified its retrieval interface and
proposed '2D-RIB' in the previous work. The aim of the present paper
is to improve his/her satisfaction level to the retrieval result in the
2D-RIB. Our method is to extend the 2D-RIB. One of our extensions is
to define and introduce the following two measures: 'melody
goodness' and 'general acceptance'. Another extension is three types
of customization menus. The result of evaluation using a pilot system
is as follows. Both of these two measures 'melody goodness'
and -general acceptance- can contribute to the improvement.
Moreover, it is effective if we introduce the customization menu
which enables a retrieval person to reduce the strictness level of
retrieval condition in an impression pair based on his/her need.
Abstract: Data mining has been used very frequently to extract
hidden information from large databases. This paper suggests the use
of decision trees for continuously extracting the clinical reasoning in
the form of medical expert-s actions that is inherent in large number
of EMRs (Electronic Medical records). In this way the extracted data
could be used to teach students of oral medicine a number of orderly
processes for dealing with patients who represent with different
problems within the practice context over time.
Abstract: A new approach is adopted in this paper based
on Turk and Pentland-s eigenface method. It was found that the
probability density function of the distance between the projection
vector of the input face image and the average projection vector of
the subject in the face database, follows Rayleigh distribution. In
order to decrease the false acceptance rate and increase the
recognition rate, the input face image has been recognized using two
thresholds including the acceptance threshold and the rejection
threshold. We also find out that the value of two thresholds will be
close to each other as number of trials increases. During the training,
in order to reduce the number of trials, the projection vectors for each
subject has been averaged. The recognition experiments using the
proposed algorithm show that the recognition rate achieves to
92.875% whilst the average number of judgment is only 2.56 times.
Abstract: The method of gait identification based on the nearest neighbor classification technique with motion similarity assessment by the dynamic time warping is proposed. The model based kinematic motion data, represented by the joints rotations coded by Euler angles and unit quaternions is used. The different pose distance functions in Euler angles and quaternion spaces are considered. To evaluate individual features of the subsequent joints movements during gait cycle, joint selection is carried out. To examine proposed approach database containing 353 gaits of 25 humans collected in motion capture laboratory is used. The obtained results are promising. The classifications, which takes into consideration all joints has accuracy over 91%. Only analysis of movements of hip joints allows to correctly identify gaits with almost 80% precision.
Abstract: In this paper, we use Radial Basis Function Networks
(RBFN) for solving the problem of environmental interference
cancellation of speech signal. We show that the Second Order Thin-
Plate Spline (SOTPS) kernel cancels the interferences effectively.
For make comparison, we test our experiments on two conventional
most used RBFN kernels: the Gaussian and First order TPS (FOTPS)
basis functions. The speech signals used here were taken from the
OGI Multi-Language Telephone Speech Corpus database and were
corrupted with six type of environmental noise from NOISEX-92
database. Experimental results show that the SOTPS kernel can
considerably outperform the Gaussian and FOTPS functions on
speech interference cancellation problem.
Abstract: In this paper, we propose an improvement of pattern
growth-based PrefixSpan algorithm, called I-PrefixSpan. The general idea of I-PrefixSpan is to use sufficient data structure for Seq-Tree
framework and separator database to reduce the execution time and
memory usage. Thus, with I-PrefixSpan there is no in-memory database stored after index set is constructed. The experimental result
shows that using Java 2, this method improves the speed of PrefixSpan up to almost two orders of magnitude as well as the memory usage to more than one order of magnitude.
Abstract: Prior research evidenced that unimodal biometric
systems have several tradeoffs like noisy data, intra-class variations,
restricted degrees of freedom, non-universality, spoof attacks, and
unacceptable error rates. In order for the biometric system to be more
secure and to provide high performance accuracy, more than one
form of biometrics are required. Hence, the need arise for multimodal
biometrics using combinations of different biometric modalities. This
paper introduces a multimodal biometric system (MMBS) based on
fusion of whole dorsal hand geometry and fingerprints that acquires
right and left (Rt/Lt) near-infra-red (NIR) dorsal hand geometry (HG)
shape and (Rt/Lt) index and ring fingerprints (FP). Database of 100
volunteers were acquired using the designed prototype. The acquired
images were found to have good quality for all features and patterns
extraction to all modalities. HG features based on the hand shape
anatomical landmarks were extracted. Robust and fast algorithms for
FP minutia points feature extraction and matching were used. Feature
vectors that belong to similar biometric traits were fused using
feature fusion methodologies. Scores obtained from different
biometric trait matchers were fused using the Min-Max
transformation-based score fusion technique. Final normalized scores
were merged using the sum of scores method to obtain a single
decision about the personal identity based on multiple independent
sources. High individuality of the fused traits and user acceptability
of the designed system along with its experimental high performance
biometric measures showed that this MMBS can be considered for
med-high security levels biometric identification purposes.
Abstract: The goal of speech parameterization is to extract the relevant information about what is being spoken from the audio signal. In speech recognition systems Mel-Frequency Cepstral Coefficients (MFCC) and Relative Spectral Mel-Frequency Cepstral Coefficients (RASTA-MFCC) are the two main techniques used. It will be shown in this paper that it presents some modifications to the original MFCC method. In our work the effectiveness of proposed changes to MFCC called Modified Function Cepstral Coefficients (MODFCC) were tested and compared against the original MFCC and RASTA-MFCC features. The prosodic features such as jitter and shimmer are added to baseline spectral features. The above-mentioned techniques were tested with impulsive signals under various noisy conditions within AURORA databases.
Abstract: Iris-based biometric authentication is gaining importance
in recent times. Iris biometric processing however, is a complex
process and computationally very expensive. In the overall processing
of iris biometric in an iris-based biometric authentication system,
feature processing is an important task. In feature processing, we extract
iris features, which are ultimately used in matching. Since there
is a large number of iris features and computational time increases
as the number of features increases, it is therefore a challenge to
develop an iris processing system with as few as possible number of
features and at the same time without compromising the correctness.
In this paper, we address this issue and present an approach to feature
extraction and feature matching process. We apply Daubechies D4
wavelet with 4 levels to extract features from iris images. These
features are encoded with 2 bits by quantizing into 4 quantization
levels. With our proposed approach it is possible to represent an
iris template with only 304 bits, whereas existing approaches require
as many as 1024 bits. In addition, we assign different weights to
different iris region to compare two iris templates which significantly
increases the accuracy. Further, we match the iris template based on
a weighted similarity measure. Experimental results on several iris
databases substantiate the efficacy of our approach.
Abstract: Skin color can provide a useful and robust cue
for human-related image analysis, such as face detection,
pornographic image filtering, hand detection and tracking,
people retrieval in databases and Internet, etc. The major
problem of such kinds of skin color detection algorithms is
that it is time consuming and hence cannot be applied to a real
time system. To overcome this problem, we introduce a new
fast technique for skin detection which can be applied in a real
time system. In this technique, instead of testing each image
pixel to label it as skin or non-skin (as in classic techniques),
we skip a set of pixels. The reason of the skipping process is
the high probability that neighbors of the skin color pixels are
also skin pixels, especially in adult images and vise versa. The
proposed method can rapidly detect skin and non-skin color
pixels, which in turn dramatically reduce the CPU time
required for the protection process. Since many fast detection
techniques are based on image resizing, we apply our
proposed pixel skipping technique with image resizing to
obtain better results. The performance evaluation of the
proposed skipping and hybrid techniques in terms of the
measured CPU time is presented. Experimental results
demonstrate that the proposed methods achieve better result
than the relevant classic method.
Abstract: Chikungunya virus (CHICKV) is an arboviruses belonging to family Tagoviridae and is transmitted to human through by mosquito (Aedes aegypti and Aedes albopictus) bite. A large outbreak of chikungunya has been reported in India between 2006 and 2007, along with several other countries from South-East Asia and for the first time in Europe. It was for the first time that the CHICKV outbreak has been reported with mortality from Reunion Island and increased mortality from Asian countries. CHICKV affects all age groups, and currently there are no specific drugs or vaccine to cure the disease. The need of antiviral agents for the treatment of CHICKV infection and the success of virtual screening against many therapeutically valuable targets led us to carry out the structure based drug design against Chikungunya nSP2 protease (PDB: 3TRK). Highthroughput virtual screening of publicly available databases, ZINC12 and BindingDB, has been carried out using the Openeye tools and Schrodinger LLC software packages. Openeye Filter program has been used to filter the database and the filtered outputs were docked using HTVS protocol implemented in GLIDE package of Schrodinger LLC. The top HITS were further used for enriching the similar molecules from the database through vROCS; a shape based screening protocol implemented in Openeye. The approach adopted has provided different scaffolds as HITS against CHICKV protease. Three scaffolds: Indole, Pyrazole and Sulphone derivatives were selected based on the docking score and synthetic feasibility. Derivatives of Pyrazole were synthesized and submitted for antiviral screening against CHICKV.
Abstract: Data warehouse is a dedicated database used for querying and reporting. Queries in this environment show special characteristics such as multidimensionality and aggregation. Exploiting the nature of queries, in this paper we propose a query driven design framework. The proposed framework is general and allows a designer to generate a schema based on a set of queries.
Abstract: This paper investigates the problem of tracking spa¬tiotemporal changes of a satellite image through the use of Knowledge Discovery in Database (KDD). The purpose of this study is to help a given user effectively discover interesting knowledge and then build prediction and decision models. Unfortunately, the KDD process for spatiotemporal data is always marked by several types of imperfections. In our paper, we take these imperfections into consideration in order to provide more accurate decisions. To achieve this objective, different KDD methods are used to discover knowledge in satellite image databases. Each method presents a different point of view of spatiotemporal evolution of a query model (which represents an extracted object from a satellite image). In order to combine these methods, we use the evidence fusion theory which considerably improves the spatiotemporal knowledge discovery process and increases our belief in the spatiotemporal model change. Experimental results of satellite images representing the region of Auckland in New Zealand depict the improvement in the overall change detection as compared to using classical methods.
Abstract: A total of twenty tensile biopsies were collected from
children undergoing tonsillectomy from teaching hospital ENT
department and Kurdistan private hospital in sulaimani city. All
biopsies were homogenized and cultured; the obtained bacterial
isolates were purified and identified by biochemical tests and VITEK
2 compact system. Among the twenty studied samples, only one
Pseudomonas putida with probability of 99% was isolated.
Antimicrobial susceptibility was carried out by disk diffusion
method, Pseudomonas putida showed resistance to all antibiotics
used except vancomycin. The isolate further subjected to PCR and
DNA sequence analysis of blaVIM gene using different set of primers
for different regions of VIM gene. The results were found to be PCR
positive for the blaVIM gene. To determine the sequence of blaVIM
gene, DNA sequencing performed. Sequence alignment of blaVIM
gene with previously recorded blaVIM gene in NCBI- database showed
that P. putida isolate have different blaVIM gene.
Abstract: Association rules are an important problem in data
mining. Massively increasing volume of data in real life databases
has motivated researchers to design novel and incremental algorithms
for association rules mining. In this paper, we propose an incremental
association rules mining algorithm that integrates shocking
interestingness criterion during the process of building the model. A
new interesting measure called shocking measure is introduced. One
of the main features of the proposed approach is to capture the user
background knowledge, which is monotonically augmented. The
incremental model that reflects the changing data and the user beliefs
is attractive in order to make the over all KDD process more
effective and efficient. We implemented the proposed approach and
experiment it with some public datasets and found the results quite
promising.
Abstract: This paper presents a new strategy of identification
and classification of pathological voices using the hybrid method
based on wavelet transform and neural networks. After speech
acquisition from a patient, the speech signal is analysed in order to
extract the acoustic parameters such as the pitch, the formants, Jitter,
and shimmer. Obtained results will be compared to those normal and
standard values thanks to a programmable database. Sounds are
collected from normal people and patients, and then classified into
two different categories. Speech data base is consists of several
pathological and normal voices collected from the national hospital
“Rabta-Tunis". Speech processing algorithm is conducted in a
supervised mode for discrimination of normal and pathology voices
and then for classification between neural and vocal pathologies
(Parkinson, Alzheimer, laryngeal, dyslexia...). Several simulation
results will be presented in function of the disease and will be
compared with the clinical diagnosis in order to have an objective
evaluation of the developed tool.
Abstract: The usual correctness condition for a schedule of
concurrent database transactions is some form of serializability of
the transactions. For general forms, the problem of deciding whether
a schedule is serializable is NP-complete. In those cases other approaches
to proving correctness, using proof rules that allow the steps
of the proof of serializability to be guided manually, are desirable.
Such an approach is possible in the case of conflict serializability
which is proved algebraically by deriving serial schedules using
commutativity of non-conflicting operations. However, conflict serializability
can be an unnecessarily strong form of serializability restricting
concurrency and thereby reducing performance. In practice,
weaker, more general, forms of serializability for extended models of
transactions are used. Currently, there are no known methods using
proof rules for proving those general forms of serializability. In this
paper, we define serializability for an extended model of partitioned
transactions, which we show to be as expressive as serializability
for general partitioned transactions. An algebraic method for proving
general serializability is obtained by giving an initial-algebra specification
of serializable schedules of concurrent transactions in the
model. This demonstrates that it is possible to conduct algebraic
proofs of correctness of concurrent transactions in general cases.