Abstract: Since the conception of JML, many tools, applications and implementations have been done. In this context, the users or developers who want to use JML seem surounded by many of these tools, applications and so on. Looking for a common infrastructure and an independent language to provide a bridge between these tools and JML, we developed an approach to embedded contracts in XML for Java: XJML. This approach offer us the ability to separate preconditions, posconditions and class invariants using JML and XML, so we made a front-end which can process Runtime Assertion Checking, Extended Static Checking and Full Static Program Verification. Besides, the capabilities for this front-end can be extended and easily implemented thanks to XML. We believe that XJML is an easy way to start the building of a Graphic User Interface delivering in this way a friendly and IDE independency to developers community wich want to work with JML.
Abstract: For over a decade, the Pulse Coupled Neural Network
(PCNN) based algorithms have been successfully used in image
interpretation applications including image segmentation. There are
several versions of the PCNN based image segmentation methods,
and the segmentation accuracy of all of them is very sensitive to the
values of the network parameters. Most methods treat PCNN
parameters like linking coefficient and primary firing threshold as
global parameters, and determine them by trial-and-error. The
automatic determination of appropriate values for linking coefficient,
and primary firing threshold is a challenging problem and deserves
further research. This paper presents a method for obtaining global as
well as local values for the linking coefficient and the primary firing
threshold for neurons directly from the image statistics. Extensive
simulation results show that the proposed approach achieves
excellent segmentation accuracy comparable to the best accuracy
obtainable by trial-and-error for a variety of images.
Abstract: Elliptic curve-based certificateless signature is slowly
gaining attention due to its ability to retain the efficiency of
identity-based signature to eliminate the need of certificate
management while it does not suffer from inherent private
key escrow problem. Generally, cryptosystem based on elliptic
curve offers equivalent security strength at smaller key sizes
compared to conventional cryptosystem such as RSA which
results in faster computations and efficient use of computing
power, bandwidth, and storage. This paper proposes to implement
certificateless signature based on bilinear pairing to
structure the framework of IKE authentication. In this paper,
we perform a comparative analysis of certificateless signature
scheme with a well-known RSA scheme and also present the
experimental results in the context of signing and verification
execution times. By generalizing our observations, we discuss the
different trade-offs involved in implementing IKE authentication
by using certificateless signature.
Abstract: In this paper, a novel multi join algorithm to join
multiple relations will be introduced. The novel algorithm is based
on a hashed-based join algorithm of two relations to produce a double index. This is done by scanning the two relations once. But
instead of moving the records into buckets, a double index will be built. This will eliminate the collision that can happen from a complete hash algorithm. The double index will be divided into join
buckets of similar categories from the two relations. The algorithm then joins buckets with similar keys to produce joined buckets. This
will lead at the end to a complete join index of the two relations. without actually joining the actual relations. The time complexity
required to build the join index of two categories is Om log m where m is the size of each category. Totaling time complexity to O n log m
for all buckets. The join index will be used to materialize the joined relation if required. Otherwise, it will be used along with other join
indices of other relations to build a lattice to be used in multi-join operations with minimal I/O requirements. The lattice of the join indices can be fitted into the main memory to reduce time complexity of the multi join algorithm.
Abstract: Negation is useful in the majority of the real world applications. However, its introduction leads to semantic and canonical problems. SEPN nets are well adapted extension of predicate nets for the definition and manipulation of stratified programs. This formalism is characterized by two main contributions. The first concerns the management of the whole class of stratified programs. The second contribution is related to usual operations optimization (maximal stratification, incremental updates ...). We propose, in this paper, useful algorithms for manipulating stratified programs using SEPN. These algorithms were implemented and validated with STRPRO tool.
Abstract: One important objective in Precision Agriculture is to minimize the volume of herbicides that are applied to the fields through the use of site-specific weed management systems. In order to reach this goal, two major factors need to be considered: 1) the similar spectral signature, shape and texture between weeds and crops; 2) the irregular distribution of the weeds within the crop's field. This paper outlines an automatic computer vision system for the detection and differential spraying of Avena sterilis, a noxious weed growing in cereal crops. The proposed system involves two processes: image segmentation and decision making. Image segmentation combines basic suitable image processing techniques in order to extract cells from the image as the low level units. Each cell is described by two area-based attributes measuring the relations among the crops and the weeds. From these attributes, a hybrid decision making approach determines if a cell must be or not sprayed. The hybrid approach uses the Support Vector Machines and the Fuzzy k-Means methods, combined through the fuzzy aggregation theory. This makes the main finding of this paper. The method performance is compared against other available strategies.
Abstract: Research on damage of gears and gear pairs using
vibration signals remains very attractive, because vibration signals
from a gear pair are complex in nature and not easy to interpret.
Predicting gear pair defects by analyzing changes in vibration signal
of gears pairs in operation is a very reliable method. Therefore, a
suitable vibration signal processing technique is necessary to extract
defect information generally obscured by the noise from dynamic
factors of other gear pairs.This article presents the value of cepstrum
analysis in vehicle gearbox fault diagnosis. Cepstrum represents the
overall power content of a whole family of harmonics and sidebands
when more than one family of sidebands is present at the same time.
The concept for the measurement and analysis involved in using the
technique are briefly outlined. Cepstrum analysis is used for detection
of an artificial pitting defect in a vehicle gearbox loaded with
different speeds and torques. The test stand is equipped with three
dynamometers; the input dynamometer serves asthe internal
combustion engine, the output dynamometers introduce the load on
the flanges of the output joint shafts. The pitting defect is
manufactured on the tooth side of a gear of the fifth speed on the
secondary shaft. Also, a method for fault diagnosis of gear faults is
presented based on order Cepstrum. The procedure is illustrated with
the experimental vibration data of the vehicle gearbox. The results
show the effectiveness of Cepstrum analysis in detection and
diagnosis of the gear condition.
Abstract: Data mining is the process of sifting through large
volumes of data, analyzing data from different perspectives and
summarizing it into useful information. One of the widely used
desktop applications for data mining is the Weka tool which is
nothing but a collection of machine learning algorithms implemented
in Java and open sourced under the General Public License (GPL). A
web service is a software system designed to support interoperable
machine to machine interaction over a network using SOAP
messages. Unlike a desktop application, a web service is easy to
upgrade, deliver and access and does not occupy any memory on the
system. Keeping in mind the advantages of a web service over a
desktop application, in this paper we are demonstrating how this Java
based desktop data mining application can be implemented as a web
service to support data mining across the internet.
Abstract: In quality control of freeze-dried durian, crispiness is
a key quality index of the product. Generally, crispy testing has to be
done by a destructive method. A nondestructive testing of the
crispiness is required because the samples can be reused for other
kinds of testing. This paper proposed a crispiness classification
method of freeze-dried durians using fuzzy logic for decision
making. The physical changes of a freeze-dried durian include the
pores appearing in the images. Three physical features including (1)
the diameters of pores, (2) the ratio of the pore area and the
remaining area, and (3) the distribution of the pores are considered to
contribute to the crispiness. The fuzzy logic is applied for making the
decision. The experimental results comparing with food expert
opinion showed that the accuracy of the proposed classification
method is 83.33 percent.
Abstract: This paper applies fuzzy set theory to evaluate the
service quality of online auction. Service quality is a composition of
various criteria. Among them many intangible attributes are difficult
to measure. This characteristic introduces the obstacles for respondent
in replying to the survey. So as to overcome this problem, we
invite fuzzy set theory into the measurement of performance. By
using AHP in obtaining criteria and TOPSIS in ranking, we found
the most concerned dimension of service quality is Transaction
Safety Mechanism and the least is Charge Item. Regarding to the
most concerned attributes are information security, accuracy and
information.
Abstract: Building intelligent traffic guide systems has been an
interesting subject recently. A good system should be able to observe
all important visual information to be able to analyze the context of
the scene. To do so, signs in general, and traffic signs in particular,
are usually taken into account as they contain rich information to
these systems. Therefore, many researchers have put an effort on
sign recognition field. Sign localization or sign detection is the most
important step in the sign recognition process. This step filters out
non informative area in the scene, and locates candidates in later
steps. In this paper, we apply a new approach in detecting sign
locations using a new color invariant model. Experiments are carried
out with different datasets introduced in other works where authors
claimed the difficulty in detecting signs under unfavorable imaging
conditions. Our method is simple, fast and most importantly it gives
a high detection rate in locating signs.
Abstract: Software reliability prediction gives a great opportunity to measure the software failure rate at any point throughout system test. A software reliability prediction model provides with the technique for improving reliability. Software reliability is very important factor for estimating overall system reliability, which depends on the individual component reliabilities. It differs from hardware reliability in that it reflects the design perfection. Main reason of software reliability problems is high complexity of software. Various approaches can be used to improve the reliability of software. We focus on software reliability model in this article, assuming that there is a time redundancy, the value of which (the number of repeated transmission of basic blocks) can be an optimization parameter. We consider given mathematical model in the assumption that in the system may occur not only irreversible failures, but also a failure that can be taken as self-repairing failures that significantly affect the reliability and accuracy of information transfer. Main task of the given paper is to find a time distribution function (DF) of instructions sequence transmission, which consists of random number of basic blocks. We consider the system software unreliable; the time between adjacent failures has exponential distribution.
Abstract: IVE toolkit has been created for facilitating research,education and development in the field of virtual storytelling and computer games. Primarily, the toolkit is intended for modelling action selection mechanisms of virtual humans, investigating level-of-detail AI techniques for large virtual environments, and for exploring joint behaviour and role-passing technique (Sec. V). Additionally, the toolkit can be used as an AI middleware without any changes. The main facility of IVE is that it serves for prototyping both the AI and virtual worlds themselves. The purpose of this paper is to describe IVE's features in general and to present our current work - including an educational game - on this platform.
Abstract: The purposes of this research are to study and develop
the algorithm of Thai spoonerism words by semi-automatic computer
programs, that is to say, in part of data input, syllables are already
separated and in part of spoonerism, the developed algorithm is
utilized, which can establish rules and mechanisms in Thai
spoonerism words for bi-syllables by utilizing analysis in elements of
the syllables, namely cluster consonant, vowel, intonation mark and
final consonant. From the study, it is found that bi-syllable Thai
spoonerism has 1 case of spoonerism mechanism, namely
transposition in value of vowel, intonation mark and consonant of
both 2 syllables but keeping consonant value and cluster word (if
any).
From the study, the rules and mechanisms in Thai spoonerism
word were applied to develop as Thai spoonerism word software,
utilizing PHP program. the software was brought to conduct a
performance test on software execution; it is found that the program
performs bi-syllable Thai spoonerism correctly or 99% of all words
used in the test and found faults on the program at 1% as the words
obtained from spoonerism may not be spelling in conformity with
Thai grammar and the answer in Thai spoonerism could be more than
1 answer.
Abstract: In Peer-to-Peer service networks, where peers offer any kind of publicly available services or applications, intuitive navigation through all services in the network becomes more difficult as the number of services increases. In this article, a concept is discussed that enables users to intuitively browse and use large scale P2P service networks. The concept extends the idea of creating virtual 3D-environments solely based on Peer-to-Peer technologies. Aside from browsing, users shall have the possibility to emphasize services of interest using their own semantic criteria. The appearance of the virtual world shall intuitively reflect network properties that may be of interest for the user. Additionally, the concept comprises options for load- and traffic-balancing. In this article, the requirements concerning the underlying infrastructure and the graphical user interface are defined. First impressions of the appearance of future systems are presented and the next steps towards a prototypical implementation are discussed.
Abstract: Color Image quantization (CQ) is an important
problem in computer graphics, image and processing. The aim of
quantization is to reduce colors in an image with minimum distortion.
Clustering is a widely used technique for color quantization; all
colors in an image are grouped to small clusters. In this paper, we
proposed a new hybrid approach for color quantization using firefly
algorithm (FA) and K-means algorithm. Firefly algorithm is a swarmbased
algorithm that can be used for solving optimization problems.
The proposed method can overcome the drawbacks of both
algorithms such as the local optima converge problem in K-means
and the early converge of firefly algorithm. Experiments on three
commonly used images and the comparison results shows that the
proposed algorithm surpasses both the base-line technique k-means
clustering and original firefly algorithm.
Abstract: Rule Discovery is an important technique for mining knowledge from large databases. Use of objective measures for discovering interesting rules lead to another data mining problem, although of reduced complexity. Data mining researchers have studied subjective measures of interestingness to reduce the volume of discovered rules to ultimately improve the overall efficiency of KDD process. In this paper we study novelty of the discovered rules as a subjective measure of interestingness. We propose a hybrid approach that uses objective and subjective measures to quantify novelty of the discovered rules in terms of their deviations from the known rules. We analyze the types of deviation that can arise between two rules and categorize the discovered rules according to the user specified threshold. We implement the proposed framework and experiment with some public datasets. The experimental results are quite promising.
Abstract: Sickness absence represents a major economic and
social issue. Analysis of sick leave data is a recurrent challenge to analysts because of the complexity of the data structure which is
often time dependent, highly skewed and clumped at zero. Ignoring these features to make statistical inference is likely to be inefficient
and misguided. Traditional approaches do not address these problems. In this study, we discuss model methodologies in terms of statistical techniques for addressing the difficulties with sick leave data. We also introduce and demonstrate a new method by performing a longitudinal assessment of long-term absenteeism using
a large registration dataset as a working example available from the Helsinki Health Study for municipal employees from Finland during the period of 1990-1999. We present a comparative study on model
selection and a critical analysis of the temporal trends, the occurrence
and degree of long-term sickness absences among municipal employees. The strengths of this working example include the large
sample size over a long follow-up period providing strong evidence in supporting of the new model. Our main goal is to propose a way to
select an appropriate model and to introduce a new methodology for analysing sickness absence data as well as to demonstrate model
applicability to complicated longitudinal data.
Abstract: This paper presents an intrusion detection system of hybrid neural network model based on RBF and Elman. It is used for anomaly detection and misuse detection. This model has the memory function .It can detect discrete and related aggressive behavior effectively. RBF network is a real-time pattern classifier, and Elman network achieves the memory ability for former event. Based on the hybrid model intrusion detection system uses DARPA data set to do test evaluation. It uses ROC curve to display the test result intuitively. After the experiment it proves this hybrid model intrusion detection system can effectively improve the detection rate, and reduce the rate of false alarm and fail.
Abstract: The performance and complexity of QoS routing depends on the complex interaction between a large set of parameters. This paper investigated the scaling properties of source-directed link-state routing in large core networks. The simulation results show that the routing algorithm, network topology, and link cost function each have a significant impact on the probability of successfully routing new connections. The experiments confirm and extend the findings of other studies, and also lend new insight designing efficient quality-of-service routing policies in large networks.