Abstract: Games can be classified as games of skill, games of chance or otherwise be classified as mixed. This paper deals with the topic of scientifically classifying mixed games as more reliant on elements of chance or elements of skill and ways to scientifically measure the amount of skill involved. This is predominantly useful for classification of games as legal or illegal in deferent jurisdictions based on the local gaming laws. We propose a novel measure of skill to chance ratio called the Game Skill Measure (GSM) and utilize it to calculate the skill component of a popular variant of Poker.
Abstract: Prime Factorization based on Quantum approach in
two phases has been performed. The first phase has been achieved at
Quantum computer and the second phase has been achieved at the
classic computer (Post Processing). At the second phase the goal is to
estimate the period r of equation xrN ≡ 1 and to find the prime factors
of the composite integer N in classic computer. In this paper we
present a method based on Randomized Approach for estimation the
period r with a satisfactory probability and the composite integer N
will be factorized therefore with the Randomized Approach even the
gesture of the period is not exactly the real period at least we can find
one of the prime factors of composite N. Finally we present some
important points for designing an Emulator for Quantum Computer
Simulation.
Abstract: In this paper, we present a new and effective image indexing technique that extracts features directly from DCT domain. Our proposed approach is an object-based image indexing. For each block of size 8*8 in DCT domain a feature vector is extracted. Then, feature vectors of all blocks of image using a k-means algorithm is clustered into groups. Each cluster represents a special object of the image. Then we select some clusters that have largest members after clustering. The centroids of the selected clusters are taken as image feature vectors and indexed into the database. Also, we propose an approach for using of proposed image indexing method in automatic image classification. Experimental results on a database of 800 images from 8 semantic groups in automatic image classification are reported.
Abstract: Sharing motivations of viral advertisements by
consumers and the impacts of these advertisements on the
perceptions for brand will be questioned in this study. Three
fundamental questions are answered in the study. These are
advertisement watching and sharing motivations of individuals,
criteria of liking viral advertisement and the impact of individual
attitudes for viral advertisement on brand perception respectively.
This study will be carried out via a viral advertisement which was
practiced in Turkey. The data will be collected by survey method and
the sample of the study consists of individuals who experienced the
practice of sample advertisement. Data will be collected by online
survey method and will be analyzed by using SPSS statistical
package program.
Recently traditional advertisement mind have been changing. New
advertising approaches which have significant impacts on consumers
have been argued. Viral advertising is a modernist advertisement
mind which offers significant advantages to brands apart from
traditional advertising channels such as television, radio and
magazines. Viral advertising also known as Electronic Word-of-
Mouth (eWOM) consists of free spread of convincing messages sent
by brands among interpersonal communication. When compared to
the traditional advertising, a more provocative thematic approach is
argued.
The foundation of this approach is to create advertisements that
are worth sharing with others by consumers. When that fact is taken
into consideration, in a manner of speaking it can also be stated that
viral advertising is media engineering.
The content worth sharing makes people being a volunteer
spokesman of a brand and strengthens the emotional bonds among
brand and consumer. Especially for some sectors in countries which
are having traditional advertising channel limitations, viral
advertising creates vital advantages.
Abstract: This paper aims to present the main instruments used
in the economic literature for measuring the price risk, pointing out
on the advantages brought by the conditional variance in this respect.
The theoretical approach will be exemplified by elaborating an
EGARCH model for the price returns of wheat, both on Romanian
and on international market. To our knowledge, no previous
empirical research, either on price risk measurement for the
Romanian markets or studies that use the ARIMA-EGARCH
methodology, have been conducted. After estimating the
corresponding models, the paper will compare the estimated
conditional variance on the two markets.
Abstract: This paper describes a novel projection algorithm, the Projection Onto Span Algorithm (POSA) for wavelet-based superresolution and removing speckle (in wavelet domain) of unknown variance from Synthetic Aperture Radar (SAR) images. Although the POSA is good as a new superresolution algorithm for image enhancement, image metrology and biometric identification, here one will use it like a tool of despeckling, being the first time that an algorithm of super-resolution is used for despeckling of SAR images. Specifically, the speckled SAR image is decomposed into wavelet subbands; POSA is applied to the high subbands, and reconstruct a SAR image from the modified detail coefficients. Experimental results demonstrate that the new method compares favorably to several other despeckling methods on test SAR images.
Abstract: Ontology Matching is a task needed in various applica-tions, for example for comparison or merging purposes. In literature,many algorithms solving the matching problem can be found, butmost of them do not consider instances at all. Mappings are deter-mined by calculating the string-similarity of labels, by recognizinglinguistic word relations (synonyms, subsumptions etc.) or by ana-lyzing the (graph) structure. Due to the facts that instances are oftenmodeled within the ontology and that the set of instances describesthe meaning of the concepts better than their meta information,instances should definitely be incorporated into the matching process.In this paper several novel instance-based matching algorithms arepresented which enhance the quality of matching results obtainedwith common concept-based methods. Different kinds of formalismsare use to classify concepts on account of their instances and finallyto compare the concepts directly.KeywordsInstances, Ontology Matching, Semantic Web
Abstract: Snake bite cases in Malaysia most often involve the
species Naja-naja and Calloselasma rhodostoma. In keeping with the
need for a rapid snake venom detection kit in a clinical setting, plate
and dot-ELISA test for the venoms of Naja-naja sumatrana,
Calloselasma rhodostoma and the cobra venom fraction V antigen
was developed. Polyclonal antibodies were raised and further used to
prepare the reagents for the dot-ELISA test kit which was tested in
mice, rabbit and virtual human models. The newly developed dot-
ELISA kit was able to detect a minimum venom concentration of
244ng/ml with cross reactivity of one antibody type. The dot-ELISA
system was sensitive and specific for all three snake venom types in
all tested animal models. The lowest minimum venom concentration
detectable was in the rabbit model, 244ng/ml of the cobra venom
fraction V antigen. The highest minimum venom concentration was
in mice, 1953ng/ml against a multitude of venoms. The developed
dot-ELISA system for the detection of three snake venom types was
successful with a sensitivity of 95.8% and specificity of 97.9%.
Abstract: The objective of the presented work is to implement the Kalman Filter into an application that reduces the influence of the environmental changes over the robot expected to navigate over a terrain of varying friction properties. The Discrete Kalman Filter is used to estimate the robot position, project the estimated current state ahead at time through time update and adjust the projected estimated state by an actual measurement at that time via the measurement update using the data coming from the infrared sensors, ultrasonic sensors and the visual sensor respectively. The navigation test has been performed in a real world environment and has been found to be robust.
Abstract: This article explores the sociological perspectives on
social problems and the role of the media which has a delicate role to
tread in balancing its duty to the public and the victim Whilst social
problems have objective conditions, it is the subjective definition of
such problems that ensure which social problem comes to the fore
and which doesn-t. Further it explores the roles and functions of
policymakers when addressing social problems and the impact of the
inception of media profiling as well as the advantages and
disadvantages of media profiling towards social problems. It focuses
on the inception of media profiling due to its length and a follow up
article will explore how current media profiling towards social
problems have evolved since its inception.
Abstract: Least Significant Bit (LSB) technique is the earliest
developed technique in watermarking and it is also the most simple,
direct and common technique. It essentially involves embedding the
watermark by replacing the least significant bit of the image data with
a bit of the watermark data. The disadvantage of LSB is that it is not
robust against attacks. In this study intermediate significant bit (ISB)
has been used in order to improve the robustness of the watermarking
system. The aim of this model is to replace the watermarked image
pixels by new pixels that can protect the watermark data against
attacks and at the same time keeping the new pixels very close to the
original pixels in order to protect the quality of watermarked image.
The technique is based on testing the value of the watermark pixel
according to the range of each bit-plane.
Abstract: Bagging and boosting are among the most popular resampling ensemble methods that generate and combine a diversity of classifiers using the same learning algorithm for the base-classifiers. Boosting algorithms are considered stronger than bagging on noisefree data. However, there are strong empirical indications that bagging is much more robust than boosting in noisy settings. For this reason, in this work we built an ensemble using a voting methodology of bagging and boosting ensembles with 10 subclassifiers in each one. We performed a comparison with simple bagging and boosting ensembles with 25 sub-classifiers, as well as other well known combining methods, on standard benchmark datasets and the proposed technique was the most accurate.
Abstract: An experimental campaign of measurements for a
Darrieus vertical-axis wind turbine (VAWT) is presented for open
field conditions. The turbine is characterized by a twisted bladed
design, each blade being placed at a fixed distance from the rotational
shaft. The experimental setup to perform the acquisitions is described.
The results are lower than expected, due to the high influence of the
wind shear.
Abstract: This paper deals with the helical flow of a Newtonian
fluid in an infinite circular cylinder, due to both longitudinal and
rotational shear stress. The velocity field and the resulting shear
stress are determined by means of the Laplace and finite Hankel
transforms and satisfy all imposed initial and boundary conditions.
For large times, these solutions reduce to the well-known steady-state
solutions.
Abstract: This paper simulates the ad-hoc mesh network in rural areas, where such networks receive great attention due to their cost, since installing the infrastructure for regular networks in these areas is not possible due to the high cost. The distance between the communicating nodes is the most obstacles that the ad-hoc mesh network will face. For example, in Terranet technology, two nodes can communicate if they are only one kilometer far from each other. However, if the distance between them is more than one kilometer, then each node in the ad-hoc mesh networks has to act as a router that forwards the data it receives to other nodes. In this paper, we try to find the critical number of nodes which makes the network fully connected in a particular area, and then propose a method to enhance the intermediate node to accept to be a router to forward the data from the sender to the receiver. Much work was done on technological changes on peer to peer networks, but the focus of this paper will be on another feature which is to find the minimum number of nodes needed for a particular area to be fully connected and then to enhance the users to switch on their phones and accept to work as a router for other nodes. Our method raises the successful calls to 81.5% out of 100% attempt calls.
Abstract: In this paper, a simple active contour based visual
tracking algorithm is presented for outdoor AGV application which is
currently under development at the USM robotic research group
(URRG) lab. The presented algorithm is computationally low cost
and able to track road boundaries in an image sequence and can
easily be implemented on available low cost hardware. The proposed
algorithm used an active shape modeling using the B-spline
deformable template and recursive curve fitting method to track the
current orientation of the road.
Abstract: This paper attempts to explore the phenomenon of metaphorization in English newspaper headlines from the perspective of pragmatic investigation. With relevance theory as the guideline, this paper makes an explanation of the processing of metaphor with a pragmatic approach and points that metaphor is the stimulus adopted by journalists to achieve optimal relevance in this ostensive communication, as well as the strategy to fulfill their writing purpose.
Abstract: Support Vector Domain Description (SVDD) is one of the best-known one-class support vector learning methods, in which one tries the strategy of using balls defined on the feature space in order to distinguish a set of normal data from all other possible abnormal objects. As all kernel-based learning algorithms its performance depends heavily on the proper choice of the kernel parameter. This paper proposes a new approach to select kernel's parameter based on maximizing the distance between both gravity centers of normal and abnormal classes, and at the same time minimizing the variance within each class. The performance of the proposed algorithm is evaluated on several benchmarks. The experimental results demonstrate the feasibility and the effectiveness of the presented method.
Abstract: The knowledge base of welding defect recognition is
essentially incomplete. This characteristic determines that the recognition results do not reflect the actual situation. It also has a further influence on the classification of welding quality. This paper is
concerned with the study of a rough set based method to reduce the influence and improve the classification accuracy. At first, a rough set
model of welding quality intelligent classification has been built. Both condition and decision attributes have been specified. Later on, groups
of the representative multiple compound defects have been chosen
from the defect library and then classified correctly to form the
decision table. Finally, the redundant information of the decision table has been reducted and the optimal decision rules have been reached. By this method, we are able to reclassify the misclassified defects to
the right quality level. Compared with the ordinary ones, this method
has higher accuracy and better robustness.
Abstract: Finding the minimal logical functions has important applications in the design of logical circuits. This task is solved by many different methods but, frequently, they are not suitable for a computer implementation. We briefly summarise the well-known Quine-McCluskey method, which gives a unique procedure of computing and thus can be simply implemented, but, even for simple examples, does not guarantee an optimal solution. Since the Petrick extension of the Quine-McCluskey method does not give a generally usable method for finding an optimum for logical functions with a high number of values, we focus on interpretation of the result of the Quine-McCluskey method and show that it represents a set covering problem that, unfortunately, is an NP-hard combinatorial problem. Therefore it must be solved by heuristic or approximation methods. We propose an approach based on genetic algorithms and show suitable parameter settings.