Abstract: This paper presents a new STAKCERT KDD
processes for worm detection. The enhancement introduced in the
data-preprocessing resulted in the formation of a new STAKCERT
model for worm detection. In this paper we explained in detail how
all the processes involved in the STAKCERT KDD processes are
applied within the STAKCERT model for worm detection. Based on
the experiment conducted, the STAKCERT model yielded a 98.13%
accuracy rate for worm detection by integrating the STAKCERT
KDD processes.
Abstract: In this paper, an automated algorithm to estimate and remove the continuous baseline from measured spectra containing both continuous and discontinuous bands is proposed. The algorithm uses previous information contained in a Continuous Database Spectra (CDBS) to obtain a linear basis, with minimum number of sampled vectors, capable of representing a continuous baseline. The proposed algorithm was tested by using a CDBS of flame spectra where Principal Components Analysis and Non-negative Matrix Factorization were used to obtain linear bases. Thus, the radical emissions of natural gas, oil and bio-oil flames spectra at different combustion conditions were obtained. In order to validate the performance in the baseline estimation process, the Goodness-of-fit Coefficient and the Root Mean-squared Error quality metrics were evaluated between the estimated and the real spectra in absence of discontinuous emission. The achieved results make the proposed method a key element in the development of automatic monitoring processes strategies involving discontinuous spectral bands.
Abstract: The overriding goal of software engineering is to
provide a high quality system, application or a product. To achieve
this goal, software engineers must apply effective methods coupled
with modern tools within the context of a mature software process
[2]. In addition, it is also must to assure that high quality is realized.
Although many quality measures can be collected at the project
levels, the important measures are errors and defects. Deriving a
quality measure for reusable components has proven to be
challenging task now a days. The results obtained from the study are
based on the empirical evidence of reuse practices, as emerged from
the analysis of industrial projects. Both large and small companies,
working in a variety of business domains, and using object-oriented
and procedural development approaches contributed towards this
study. This paper proposes a quality metric that provides benefit at
both project and process level, namely defect removal efficiency
(DRE).
Abstract: The article examines the methods of protection of
citizens' personal data on the Internet using biometric identity
authentication technology. It`s celebrated their potential danger due
to the threat of loss of base biometric templates. To eliminate the
threat of compromised biometric templates is proposed to use neural
networks large and extra-large sizes, which will on the one hand
securely (Highly reliable) to authenticate a person by his biometrics,
and on the other hand make biometrics a person is not available for
observation and understanding. This article also describes in detail
the transformation of personal biometric data access code. It`s formed
the requirements for biometrics converter code for his work with the
images of "Insider," "Stranger", all the "Strangers". It`s analyzed the
effect of the dimension of neural networks on the quality of
converters mystery of biometrics in access code.
Abstract: The inherent iterative nature of product design and development poses significant challenge to reduce the product design and development time (PD). In order to shorten the time to market, organizations have adopted concurrent development where multiple specialized tasks and design activities are carried out in parallel. Iterative nature of work coupled with the overlap of activities can result in unpredictable time to completion and significant rework. Many of the products have missed the time to market window due to unanticipated or rather unplanned iteration and rework. The iterative and often overlapped processes introduce greater amounts of ambiguity in design and development, where the traditional methods and tools of project management provide less value. In this context, identifying critical metrics to understand the iteration probability is an open research area where significant contribution can be made given that iteration has been the key driver of cost and schedule risk in PD projects. Two important questions that the proposed study attempts to address are: Can we predict and identify the number of iterations in a product development flow? Can we provide managerial insights for a better control over iteration? The proposal introduces the concept of decision points and using this concept intends to develop metrics that can provide managerial insights into iteration predictability. By characterizing the product development flow as a network of decision points, the proposed research intends to delve further into iteration probability and attempts to provide more clarity.
Abstract: Software reliability, defined as the probability of a
software system or application functioning without failure or errors
over a defined period of time, has been an important area of research
for over three decades. Several research efforts aimed at developing
models to improve reliability are currently underway. One of the
most popular approaches to software reliability adopted by some of
these research efforts involves the use of operational profiles to
predict how software applications will be used. Operational profiles
are a quantification of usage patterns for a software application. The
research presented in this paper investigates an innovative multiagent
framework for automatic creation and management of
operational profiles for generic distributed systems after their release
into the market. The architecture of the proposed Operational Profile
MAS (Multi-Agent System) is presented along with detailed
descriptions of the various models arrived at following the analysis
and design phases of the proposed system. The operational profile in
this paper is extended to comprise seven different profiles. Further,
the criticality of operations is defined using a new composed metrics
in order to organize the testing process as well as to decrease the time
and cost involved in this process. A prototype implementation of the
proposed MAS is included as proof-of-concept and the framework is
considered as a step towards making distributed systems intelligent
and self-managing.
Abstract: This manuscript presents, palmprint recognition by
combining different texture extraction approaches with high accuracy.
The Region of Interest (ROI) is decomposed into different frequencytime
sub-bands by wavelet transform up-to two levels and only the
approximate image of two levels is selected, which is known as
Approximate Image ROI (AIROI). This AIROI has information of
principal lines of the palm. The Competitive Index is used as the
features of the palmprint, in which six Gabor filters of different
orientations convolve with the palmprint image to extract the orientation
information from the image. The winner-take-all strategy
is used to select dominant orientation for each pixel, which is
known as Competitive Index. Further, PCA is applied to select highly
uncorrelated Competitive Index features, to reduce the dimensions of
the feature vector, and to project the features on Eigen space. The
similarity of two palmprints is measured by the Euclidean distance
metrics. The algorithm is tested on Hong Kong PolyU palmprint
database. Different AIROI of different wavelet filter families are also
tested with the Competitive Index and PCA. AIROI of db7 wavelet
filter achievs Equal Error Rate (EER) of 0.0152% and Genuine
Acceptance Rate (GAR) of 99.67% on the palm database of Hong
Kong PolyU.
Abstract: Class cohesion is an important object-oriented
software quality attribute. It indicates how much the members in a
class are related. Assessing the class cohesion and improving the
class quality accordingly during the object-oriented design phase
allows for cheaper management of the later phases. In this paper, the
notion of distance between pairs of methods and pairs of attribute
types in a class is introduced and used as a basis for introducing a
novel class cohesion metric. The metric considers the methodmethod,
attribute-attribute, and attribute-method direct interactions.
It is shown that the metric gives more sensitive values than other
well-known design-based class cohesion metrics.
Abstract: Despite the extensive use of eLearning systems, there
is no consensus on a standard framework for evaluating this kind of
quality system. Hence, there is only a minimum set of tools that can
supervise this judgment and gives information about the course
content value. This paper presents two kinds of quality set evaluation
indicators for eLearning courses based on the computational process
of three known metrics, the Euclidian, Hamming and Levenshtein
distances. The “distance" calculus is applied to standard evaluation
templates (i.e. the European Commission Programme procedures vs.
the AFNOR Z 76-001 Standard), determining a reference point in the
evaluation of the e-learning course quality vs. the optimal concept(s).
The case study, based on the results of project(s) developed in the
framework of the European Programme “Leonardo da Vinci", with
Romanian contractors, try to put into evidence the benefits of such a
method.
Abstract: In current common research reports, salient regions
are usually defined as those regions that could present the main
meaningful or semantic contents. However, there are no uniform
saliency metrics that could describe the saliency of implicit image
regions. Most common metrics take those regions as salient regions,
which have many abrupt changes or some unpredictable
characteristics. But, this metric will fail to detect those salient useful
regions with flat textures. In fact, according to human semantic
perceptions, color and texture distinctions are the main characteristics
that could distinct different regions. Thus, we present a novel saliency
metric coupled with color and texture features, and its corresponding
salient region extraction methods. In order to evaluate the
corresponding saliency values of implicit regions in one image, three
main colors and multi-resolution Gabor features are respectively used
for color and texture features. For each region, its saliency value is
actually to evaluate the total sum of its Euclidean distances for other
regions in the color and texture spaces. A special synthesized image
and several practical images with main salient regions are used to
evaluate the performance of the proposed saliency metric and other
several common metrics, i.e., scale saliency, wavelet transform
modulus maxima point density, and important index based metrics.
Experiment results verified that the proposed saliency metric could
achieve more robust performance than those common saliency
metrics.
Abstract: In this paper a new approach to face recognition is
presented that achieves double dimension reduction, making the
system computationally efficient with better recognition results and
out perform common DCT technique of face recognition. In pattern
recognition techniques, discriminative information of image
increases with increase in resolution to a certain extent, consequently
face recognition results change with change in face image resolution
and provide optimal results when arriving at a certain resolution
level. In the proposed model of face recognition, initially image
decimation algorithm is applied on face image for dimension
reduction to a certain resolution level which provides best
recognition results. Due to increased computational speed and feature
extraction potential of Discrete Cosine Transform (DCT), it is
applied on face image. A subset of coefficients of DCT from low to
mid frequencies that represent the face adequately and provides best
recognition results is retained. A tradeoff between decimation factor,
number of DCT coefficients retained and recognition rate with
minimum computation is obtained. Preprocessing of the image is
carried out to increase its robustness against variations in poses and
illumination level. This new model has been tested on different
databases which include ORL , Yale and EME color database.
Abstract: The aim of every software product is to achieve an
appropriate level of software quality. Developers and designers are
trying to produce readable, reliable, maintainable, reusable and
testable code. To help achieve these goals, several approaches have
been utilized. In this paper, refactoring technique was used to
evaluate software quality with a quality index. It is composed of
different metric sets which describes various quality aspects.
Abstract: Web 2.0 (social networking, blogging and online
forums) can serve as a data source for social science research because
it contains vast amount of information from many different users.
The volume of that information has been growing at a very high rate
and becoming a network of heterogeneous data; this makes things
difficult to find and is therefore not almost useful. We have proposed
a novel theoretical model for gathering and processing data from
Web 2.0, which would reflect semantic content of web pages in
better way. This article deals with the analysis part of the model and
its usage for content analysis of blogs. The introductory part of the
article describes methodology for the gathering and processing data
from blogs. The next part of the article is focused on the evaluation
and content analysis of blogs, which write about specific trend.
Abstract: This paper presents a boarding on biometric
authentication through the Keystrokes Dynamics that it intends to
identify a person from its habitual rhythm to type in conventional
keyboard. Seven done experiments: verifying amount of prototypes,
threshold, features and the variation of the choice of the times of the
features vector. The results show that the use of the Keystroke
Dynamics is simple and efficient for personal authentication, getting
optimum resulted using 90% of the features with 4.44% FRR and 0%
FAR.
Abstract: In this paper, subtractive clustering based fuzzy inference system approach is used for early detection of faults in the function oriented software systems. This approach has been tested with real time defect datasets of NASA software projects named as PC1 and CM1. Both the code based model and joined model (combination of the requirement and code based metrics) of the datasets are used for training and testing of the proposed approach. The performance of the models is recorded in terms of Accuracy, MAE and RMSE values. The performance of the proposed approach is better in case of Joined Model. As evidenced from the results obtained it can be concluded that Clustering and fuzzy logic together provide a simple yet powerful means to model the earlier detection of faults in the function oriented software systems.
Abstract: The wireless adhoc network is comprised of wireless
node which can move freely and are connected among themselves
without central infrastructure. Due to the limited transmission range
of wireless interfaces, in most cases communication has to be relayed
over intermediate nodes. Thus, in such multihop network each node
(also called router) is independent, self-reliant and capable to route
the messages over the dynamic network topology. Various protocols
are reported in this field and it is very difficult to decide the best one.
A key issue in deciding which type of routing protocol is best for
adhoc networks is the communication overhead incurred by the
protocol. In this paper STAR a table driven and DSR on demand
protocols based on IEEE 802.11 are analyzed for their performance
on different performance measuring metrics versus varying traffic
CBR load using QualNet 5.0.2 network simulator.
Abstract: Can biometrics do what everyone is expecting it will?
And more importantly, should it be doing it? Biometrics is the
buzzword “on the mouth" of everyone, who are trying to use this
technology in a variety of applications. But all this “hype" about
biometrics can be dangerous without a careful evaluation of the real
needs of each application. In this paper I-ll try to focus on the
dangers of using the right technology at the right time in the wrong
place.
Abstract: As the majority of faults are found in a few of its modules so there is a need to investigate the modules that are affected severely as compared to other modules and proper maintenance need to be done on time especially for the critical applications. In this paper, we have explored the different predictor models to NASA-s public domain defect dataset coded in Perl programming language. Different machine learning algorithms belonging to the different learner categories of the WEKA project including Mamdani Based Fuzzy Inference System and Neuro-fuzzy based system have been evaluated for the modeling of maintenance severity or impact of fault severity. The results are recorded in terms of Accuracy, Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). The results show that Neuro-fuzzy based model provides relatively better prediction accuracy as compared to other models and hence, can be used for the maintenance severity prediction of the software.
Abstract: Importance of software quality is increasing leading to development of new sophisticated techniques, which can be used in constructing models for predicting quality attributes. One such technique is Artificial Neural Network (ANN). This paper examined the application of ANN for software quality prediction using Object- Oriented (OO) metrics. Quality estimation includes estimating maintainability of software. The dependent variable in our study was maintenance effort. The independent variables were principal components of eight OO metrics. The results showed that the Mean Absolute Relative Error (MARE) was 0.265 of ANN model. Thus we found that ANN method was useful in constructing software quality model.
Abstract: The last decade has shown that object-oriented
concept by itself is not that powerful to cope with the rapidly
changing requirements of ongoing applications. Component-based
systems achieve flexibility by clearly separating the stable parts of
systems (i.e. the components) from the specification of their
composition. In order to realize the reuse of components effectively
in CBSD, it is required to measure the reusability of components.
However, due to the black-box nature of components where the
source code of these components are not available, it is difficult to
use conventional metrics in Component-based Development as these
metrics require analysis of source codes. In this paper, we survey
few existing component-based reusability metrics. These metrics
give a border view of component-s understandability, adaptability,
and portability. It also describes the analysis, in terms of quality
factors related to reusability, contained in an approach that aids
significantly in assessing existing components for reusability.