Abstract: This study was conducted to investigate the extent
with which knowledge of results influences the performance of
cricket players. A sample of 160 fresh students in the Department of
Physical and Health Education who are novice in the game were
randomly assigned into two groups. The first group of eighty (80)
subjects was classified as experimental group while the second group
of eighty (80) subjects was the control group. Subjects in both groups
were asked to bowl and bat ten times each for a period of six weeks.
After the first round, the subjects in the experimental group were
allowed feedback on their performance in the first trial while those in
the control group were denied feedback. Two null hypotheses
generated for the study were tested using percentages and chi-square
statistical analysis at 0.05 level of significance. Analysis of data
showed that knowledge of results influenced the performance of
cricket players. It was concluded that knowledge of results is
pertinent for effective skill acquisition and could enhance better
performance among unskilled cricket players. Hence, it is suggested
that immediate feedback on the level of skill acquisition by the
prospective and unskilled cricket players would inspire them for
better performance in cricket tournaments.
Abstract: The present study seeks to investigate the application
of expansion strategy in Persian subtitles of English crime movies.
More precisely, this study aims at classifying the different types of
expansion used in subtitles as well as investigating the
appropriateness or inappropriateness of the application of each type.
To achieve this end, three movies; namely, The Net (1995), Contact
(1997) and Mission Impossible 2 (2000), available with Persian
subtitles, were selected for the study. To collect the data, the above
mentioned movies were watched and those parts of the Persian
subtitles in which expansion had been used were identified and
extracted along with their English dialogs. Then, the extracted
Persian subtitles were classified based on the reason that led to
expansion in each case. Next, the appropriateness or
inappropriateness of using expansion in the extracted Persian
subtitles was descriptively investigated. Finally, an equivalent not
containing any expansion was proposed for those cases in which the
meaning could be fully transferred without this strategy. The findings
of the study indicated that the reasons range from explicitation
(explicitation of visual, co-textual and contextual information),
mistranslation and paraphrasing to the preferences of subtitlers.
Furthermore, it was found that the employment of expansion strategy
was inappropriate in all cases except for those caused by explicitation
of contextual information since correct and shorter equivalents which
were equally capable of conveying the intended meaning could be
posited for the original dialogs.
Abstract: In the context of the handwriting recognition, we
propose an off line system for the recognition of the Arabic
handwritten words of the Algerian departments. The study is based
mainly on the evaluation of neural network performances, trained
with the gradient back propagation algorithm. The used parameters to
form the input vector of the neural network are extracted on the
binary images of the handwritten word by several methods. The
Distribution parameters, the centered moments of the different
projections of the different segments, the centered moments of the
word image coding according to the directions of Freeman, and the
Barr features applied binary image of the word and on its different
segments. The classification is achieved by a multi layers perceptron.
A detailed experiment is carried and satisfactory recognition results
are reported.
Abstract: It is well-known that, using principal weak flatness
property, some important monoids are characterized, such as regular
monoids, left almost regular monoids, and so on. In this article, we
define a generalization of principal weak flatness called GP-Flatness,
and will characterize monoids by this property of their right (Rees
factor) acts. Also we investigate new classes of monoids called
generally regular monoids and generally left almost regular monoids.
Abstract: In this paper, we present an optimization technique or
a learning algorithm using the hybrid architecture by combining the
most popular sequence recognition models such as Recurrent Neural
Networks (RNNs) and Hidden Markov models (HMMs). In order to
improve the sequence/pattern recognition/classification performance
by applying a hybrid/neural symbolic approach, a gradient descent
learning algorithm is developed using the Real Time Recurrent
Learning of Recurrent Neural Network for processing the knowledge
represented in trained Hidden Markov Models. The developed hybrid
algorithm is implemented on automata theory as a sample test beds
and the performance of the designed algorithm is demonstrated and
evaluated on learning the deterministic finite state automata.
Abstract: Dead wood and habitat tree such as fallen logs, snags,
stumps and cracks and loos bark etc. are regarded as an important
ecological component of forests on which many forest dwelling
species depend on presence of them within forest ecosystems.
Meanwhile its relation to management history in Caspian forest has
gone unreported. The aim of research was to compare the amounts of
dead wood and habitat trees in the forests with historically different
intensities of management, including: forests with the long term
implication of management (PS), the short term implication of
management (NS) which were compared with semi virgin forest
(GS). The number of 405 individual dead and habitat trees were
recorded and measured at 109 sampling locations. ANOVA revealed
volume of dead tree in the form and decay classes significantly differ
within sites and dead volume in the semi virgin forest significantly
higher than managed sites. Comparing the amount of dead and
habitat tree in three sites showed that, dead tree volume related with
management history and significantly differ in three study sites.
Meanwhile, frequency of habitat trees was significantly different
within sites. The highest amount of habitat trees including cavities,
cracks and loose bark and fork split trees was recorded in virgin site
and lowest recorded in the sites with the long term implication of
management. It can be concluded that forest management cause
reduction of the amount of dead and habitat tree specially in a large
size, thus managing this forest according to ecological sustainable
principles require a commitment to maintaining stand structure that
allow, continued generation of dead trees in a full range of size.
Abstract: Rice Husk (RH) is the major byproduct in the
processing of paddy rice. The management of this waste has become
a big challenge to some of the rice producers, some of these wastes
are left in open dumps while some are burn in the open space, and
these two actions have been contributing to environmental pollution.
This study evaluates an alternative waste management of this
agricultural product for use as a civil engineering material. The RH
was burn in a controlled environment to form Rice Husk Ash (RHA).
The RHA was mix with lateritic clay at 0, 2, 4, 6, 8, and 10%
proportion by weight. Chemical test was conducted on the open burn
and controlled burn RHA with the lateritic clay. Physical test such as
particle size distribution, Atterberg limits test, and density test were
carried out on the mix material. The chemical composition obtained
for the RHA showed that the total percentage compositions of Fe2O3,
SiO2 and Al2O3 were found to be above 70% (class “F” pozzolan)
which qualifies it as a very good pozzolan. The coefficient of
uniformity (Cu) was 8 and coefficient of curvature (Cc) was 2 for the
soil sample. The Plasticity Index (PI) for the 0, 2, 4, 6, 8. 10% was
21.0, 18.8, 16.7, 14.4, 12.4 and 10.7 respectively. The work
concluded that RHA can be effectively used in hydraulic barriers and
as a stabilizing agent in soil stabilization.
Abstract: Recently, numerous documents including large
volumes of unstructured data and text have been created because of the
rapid increase in the use of social media and the Internet. Usually,
these documents are categorized for the convenience of users. Because
the accuracy of manual categorization is not guaranteed, and such
categorization requires a large amount of time and incurs huge costs.
Many studies on automatic categorization have been conducted to help
mitigate the limitations of manual categorization. Unfortunately, most
of these methods cannot be applied to categorize complex documents
with multiple topics because they work on the assumption that
individual documents can be categorized into single categories only.
Therefore, to overcome this limitation, some studies have attempted to
categorize each document into multiple categories. However, the
learning process employed in these studies involves training using a
multi-categorized document set. These methods therefore cannot be
applied to the multi-categorization of most documents unless
multi-categorized training sets using traditional multi-categorization
algorithms are provided. To overcome this limitation, in this study, we
review our novel methodology for extending the category of a
single-categorized document to multiple categorizes, and then
introduce a survey-based verification scenario for estimating the
accuracy of our automatic categorization methodology.
Abstract: Clustering is a process of grouping objects and data
into groups of clusters to ensure that data objects from the same
cluster are identical to each other. Clustering algorithms in one of the
area in data mining and it can be classified into partition, hierarchical,
density based and grid based. Therefore, in this paper we do survey
and review four major hierarchical clustering algorithms called
CURE, ROCK, CHAMELEON and BIRCH. The obtained state of
the art of these algorithms will help in eliminating the current
problems as well as deriving more robust and scalable algorithms for
clustering.
Abstract: In this paper, we present a comparative study of three
methods of 2D face recognition system such as: Iso-Geodesic Curves
(IGC), Geodesic Distance (GD) and Geodesic-Intensity Histogram
(GIH). These approaches are based on computing of geodesic
distance between points of facial surface and between facial curves.
In this study we represented the image at gray level as a 2D surface in
a 3D space, with the third coordinate proportional to the intensity
values of pixels. In the classifying step, we use: Neural Networks
(NN), K-Nearest Neighbor (KNN) and Support Vector Machines
(SVM). The images used in our experiments are from two wellknown
databases of face images ORL and YaleB. ORL data base was
used to evaluate the performance of methods under conditions where
the pose and sample size are varied, and the database YaleB was used
to examine the performance of the systems when the facial
expressions and lighting are varied.
Abstract: Due to the fast and flawless technological innovation
there is a tremendous amount of data dumping all over the world in
every domain such as Pattern Recognition, Machine Learning, Spatial
Data Mining, Image Analysis, Fraudulent Analysis, World Wide
Web etc., This issue turns to be more essential for developing several
tools for data mining functionalities. The major aim of this paper is to
analyze various tools which are used to build a resourceful analytical
or descriptive model for handling large amount of information more
efficiently and user friendly. In this survey the diverse tools are
illustrated with their extensive technical paradigm, outstanding
graphical interface and inbuilt multipath algorithms in which it is
very useful for handling significant amount of data more indeed.
Abstract: Target of this study was the analysis of the impact of
crude glycerol on canine spermatozoa motility, morphology,
viability, and membrane integrity. Experiments were realized in vitro.
In the study, semen from 5 large dog breeds was used. They were
typical representatives of large breeds, coming from healthy rearing,
regularly vaccinated and integrated to the further breeding. Semen
collections were realized at the owners of animals and in the
veterinary clinic. Subsequently the experiments were realized at the
Department of Animal Physiology of the SUA in Nitra. The
spermatozoa motility was evaluated using CASA analyzer
(SpermVisionTM, Minitub, Germany) at the temperature 5 and 37°C
for 5 hours. In the study, 13 motility parameters were evaluated.
Generally, crude glycerol has generally negative effect on
spermatozoa motility. Morphological analysis was realized using
Hancock staining and the preparations were evaluated at
magnification 1000x using classification tables of morphologically
changed spermatozoa. Data clearly detected the highest number of
morphologically changed spermatozoa in the experimental groups
(know twisted tails, tail torso and tail coiling). For acrosome
alterations swelled acrosomes, removed acrosomes and acrosomes
with undulated membrane were detected. In this study also the effect
of crude glycerol on spermatozoa membrane integrity were analyzed.
The highest crude glycerol concentration significantly affects
spermatozoa integrity. Results of this study show that crude glycerol
has effect of spermatozoa motility, viability, and membrane integrity.
Detected changes are related to crude glycerol concentration,
temperature, as well as time of incubation.
Abstract: The McEliece cryptosystem is an asymmetric type of
cryptography based on error correction code. The classical McEliece
used irreducible binary Goppa code which considered unbreakable
until now especially with parameter [1024, 524, and 101], but it is
suffering from large public key matrix which leads to be difficult to
be used practically. In this work Irreducible and Separable Goppa
codes have been introduced. The Irreducible and Separable Goppa
codes used are with flexible parameters and dynamic error vectors. A
Comparison between Separable and Irreducible Goppa code in
McEliece Cryptosystem has been done. For encryption stage, to get
better result for comparison, two types of testing have been chosen;
in the first one the random message is constant while the parameters
of Goppa code have been changed. But for the second test, the
parameters of Goppa code are constant (m=8 and t=10) while the
random message have been changed. The results show that the time
needed to calculate parity check matrix in separable are higher than
the one for irreducible McEliece cryptosystem, which is considered
expected results due to calculate extra parity check matrix in
decryption process for g2(z) in separable type, and the time needed to
execute error locator in decryption stage in separable type is better
than the time needed to calculate it in irreducible type. The proposed
implementation has been done by Visual studio C#.
Abstract: The Portuguese footwear industry had in the last five years a remarkable performance in the exportation values, the trade balance and others economic indicators. After a long period of difficulties and with a strong reduction of companies and employees since 1994 until 2009, the Portuguese footwear industry changed the strategy and is now a success case between the international players of footwear. Only the Italian industry sells footwear with a higher value than the Portuguese and the distance between them is decreasing year by year. This paper analyses how the Portuguese footwear companies innovate and make innovation, according the classification proposed by the Oslo Manual. Also, analyses the strategy follow in the innovation process and shows the linkage between the type of innovation and the strategy of innovation. The research methodology was qualitative and the strategy for data collection was the case study. The qualitative data will be analyzed with the MAXQDA software. The economic results of the footwear companies studied shows differences between all of them and these differences are related with the innovation strategy adopted. The companies focused in product and marketing innovation, oriented to their target market, have higher ratios “turnover per worker” than the companies focused in process innovation. However, all the footwear companies in this “low-tech” industry create value and contribute to a positive foreign trade of 1.310 million euros in 2013. The growth strategies implemented has the participation of the sectorial organizations in several innovative projects. And it’s obvious that cooperation between all of them is a critical element to the performance achieved by the companies and the innovation observed. The Portuguese footwear sector has in the last years an excellent performance (economic results, exportation values, trade balance, brands and international image) and his performance is strongly related with the strategy in innovation followed, the type of innovation and the networks in the cluster. A simplified model, called “Ace of Diamonds”, is proposed by the authors and explains the way how this performance was reached by the seven companies that participate in the study (two of them are the leaders in the setor), and if this model can be used in others traditional and “low-tech” industries.
Abstract: This paper suggests a new internal architecture of
holon based on feature selection model using the combination of
Bees Algorithm (BA) and Artificial Neural Network (ANN). BA is
used to generate features while ANN is used as a classifier to
evaluate the produced features. Proposed system is applied on the
Wine dataset, the statistical result proves that the proposed system is
effective and has the ability to choose informative features with high
accuracy.
Abstract: Tamil handwritten document is taken as a key source
of data to identify the writer. Tamil is a classical language which has
247 characters include compound characters, consonants, vowels and
special character. Most characters of Tamil are multifaceted in
nature. Handwriting is a unique feature of an individual. Writer may
change their handwritings according to their frame of mind and this
place a risky challenge in identifying the writer. A new
discriminative model with pooled features of handwriting is proposed
and implemented using support vector machine. It has been reported
on 100% of prediction accuracy by RBF and polynomial kernel based
classification model.
Abstract: In some applications, such as image recognition or
compression, segmentation refers to the process of partitioning a
digital image into multiple segments. Image segmentation is typically
used to locate objects and boundaries (lines, curves, etc.) in images.
Image segmentation is to classify or cluster an image into several
parts (regions) according to the feature of image, for example, the
pixel value or the frequency response. More precisely, image
segmentation is the process of assigning a label to every pixel in an
image such that pixels with the same label share certain visual
characteristics. The result of image segmentation is a set of segments
that collectively cover the entire image, or a set of contours extracted
from the image. Several image segmentation algorithms were
proposed to segment an image before recognition or compression. Up
to now, many image segmentation algorithms exist and be
extensively applied in science and daily life. According to their
segmentation method, we can approximately categorize them into
region-based segmentation, data clustering, and edge-base
segmentation. In this paper, we give a study of several popular image
segmentation algorithms that are available.
Abstract: Tamil handwritten document is taken as a key source of data to identify the writer. Tamil is a classical language which has 247 characters include compound characters, consonants, vowels and special character. Most characters of Tamil are multifaceted in nature. Handwriting is a unique feature of an individual. Writer may change their handwritings according to their frame of mind and this place a risky challenge in identifying the writer. A new discriminative model with pooled features of handwriting is proposed and implemented using support vector machine. It has been reported on 100% of prediction accuracy by RBF and polynomial kernel based classification model.
Abstract: The paper identifies the features of Polish sports clubs
in the particular organizational forms: profit and nonprofit.
Identification and description of these features is carried out in terms
of financial efficiency of the given organizational form. Under the
terms of the efficiency the research allows you to specify the
advantages of particular organizational sports club form and the
following limitations. Paper considers features of sports clubs in
range of Polish conditions as legal regulations. The sources of the
functioning efficiency of sports clubs may lie in the organizational
forms in which they operate. Each of the available forms can be
considered either a for-profit or nonprofit enterprise. Depending on
this classification there are different capabilities of increasing
organizational and financial efficiency of a given sports club. Authors
start with general classification and difference between for-profit and
non-profit sport clubs. Next identifies specific financial and
organizational conditions of both organizational form and then show
examples of mixed activity forms and their efficiency effect.
Abstract: This study examined the mental health and behavioral
problems in early adolescence with the instrument of Achenbach
System of Empirically Based Assessment (ASEBA). The purpose of
the study was stratified sampling method was used to collect data
from 1975 participants. Multiple regression models and hierarchical
regression models were applied to examine the relations between the
background variables and internalizing problems, and the ones
between students’ performance and internalizing problems. The
results indicated that several background variables as predictors could
significantly predict the anxious/depressed problem; reading and
social study scores could significantly predict the anxious/depressed
problem. However the class as a hierarchical macro factor did not
indicate the significant effect. In brief, the majority of these models
represented that the background variables, behaviors and academic
performance were significantly related to the anxious/depressed
problem.