Abstract: This paper describes the problem of building secure
computational services for encrypted information in the Cloud
Computing without decrypting the encrypted data; therefore, it meets
the yearning of computational encryption algorithmic aspiration
model that could enhance the security of big data for privacy,
confidentiality, availability of the users. The cryptographic model
applied for the computational process of the encrypted data is the
Fully Homomorphic Encryption Scheme. We contribute a theoretical
presentations in a high-level computational processes that are based
on number theory and algebra that can easily be integrated and
leveraged in the Cloud computing with detail theoretic mathematical
concepts to the fully homomorphic encryption models. This
contribution enhances the full implementation of big data analytics
based cryptographic security algorithm.
Abstract: The detection of moving objects from a video image
sequences is very important for object tracking, activity recognition,
and behavior understanding in video surveillance.
The most used approach for moving objects detection / tracking is
background subtraction algorithms. Many approaches have been
suggested for background subtraction. But, these are illumination
change sensitive and the solutions proposed to bypass this problem
are time consuming.
In this paper, we propose a robust yet computationally efficient
background subtraction approach and, mainly, focus on the ability to
detect moving objects on dynamic scenes, for possible applications in
complex and restricted access areas monitoring, where moving and
motionless persons must be reliably detected. It consists of three
main phases, establishing illumination changes invariance,
background/foreground modeling and morphological analysis for
noise removing.
We handle illumination changes using Contrast Limited Histogram
Equalization (CLAHE), which limits the intensity of each pixel to
user determined maximum. Thus, it mitigates the degradation due to
scene illumination changes and improves the visibility of the video
signal. Initially, the background and foreground images are extracted
from the video sequence. Then, the background and foreground
images are separately enhanced by applying CLAHE.
In order to form multi-modal backgrounds we model each channel
of a pixel as a mixture of K Gaussians (K=5) using Gaussian Mixture
Model (GMM). Finally, we post process the resulting binary
foreground mask using morphological erosion and dilation
transformations to remove possible noise.
For experimental test, we used a standard dataset to challenge the
efficiency and accuracy of the proposed method on a diverse set of
dynamic scenes.
Abstract: In this paper we are presenting some spamming
techniques their behaviour and possible solutions. We have analyzed
how Spammers enters into online social networking sites (OSNSs) to
target them and diverse techniques used by them for this purpose.
Spamming is very common issue in present era of Internet
especially through Online Social Networking Sites (like Facebook,
Twitter, and Google+ etc.). Spam messages keep wasting Internet
bandwidth and the storage space of servers. On social networking
sites; spammers often disguise themselves by creating fake accounts
and hijacking user’s accounts for personal gains. They behave like
normal user and they continue to change their spamming strategy.
Following spamming techniques are discussed in this paper like
clickjacking, social engineered attacks, cross site scripting, URL
shortening, and drive by download. We have used elgg framework
for demonstration of some of spamming threats and respective
implementation of solutions.
Abstract: Nowadays, huge amount of multimedia repositories
make the browsing, retrieval and delivery of video contents very slow
and even difficult tasks. Video summarization has been proposed to
improve faster browsing of large video collections and more efficient
content indexing and access. In this paper, we focus on approaches to
video summarization. The video summaries can be generated in many
different forms. However, two fundamentals ways to generate
summaries are static and dynamic. We present different techniques
for each mode in the literature and describe some features used for
generating video summaries. We conclude with perspective for
further research.
Abstract: Attributes and methods are the basic contents of an
object-oriented class. The connectivity among these class members
and the relationship between the class and other classes play an
important role in determining the quality of an object-oriented
system. Class cohesion evaluates the degree of relatedness of class
attributes and methods, whereas class coupling refers to the degree to
which a class is related to other classes. Researchers have proposed
several class cohesion and class coupling measures. However, the
correlation between class coupling and class cohesion measures has
not been thoroughly studied. In this paper, using classes of three
open-source Java systems, we empirically investigate the correlation
between several measures of connectivity-based class cohesion and
coupling. Four connectivity-based cohesion measures and eight
coupling measures are considered in the empirical study. The
empirical study results show that class connectivity-based cohesion
and coupling internal quality attributes are inversely correlated. The
strength of the correlation depends highly on the cohesion and
coupling measurement approaches.
Abstract: Customer churn prediction is one of the most useful
areas of study in customer analytics. Due to the enormous amount
of data available for such predictions, machine learning and data
mining have been heavily used in this domain. There exist many
machine learning algorithms directly applicable for the problem of
customer churn prediction, and here, we attempt to experiment on
a novel approach by using a cognitive learning based technique in
an attempt to improve the results obtained by using a combination
of supervised learning methods, with cognitive unsupervised learning
methods.
Abstract: Theory of interpretation of electromagnetic fields studied in the electrical prospecting with direct current is mainly developed for the case of a horizontal surface observation. However in practice we often have to work in difficult terrain surface. Conducting interpretation without the influence of topography can cause non-existent anomalies on sections. This raises the problem of studying the impact of different shapes of ground surface relief on the results of electrical prospecting's research. This research examines the numerical solutions of the direct problem of electrical prospecting for two-dimensional and three-dimensional media, taking into account the terrain. The problem is solved using the method of integral equations. The density of secondary currents on the relief surface is obtained.
Abstract: The effects of hypertension are often lethal thus its
early detection and prevention is very important for everybody. In
this paper, a neural network (NN) model was developed and trained
based on a dataset of hypertension causative parameters in order to
forecast the likelihood of occurrence of hypertension in patients. Our
research goal was to analyze the potential of the presented NN to
predict, for a period of time, the risk of hypertension or the risk of
developing this disease for patients that are or not currently
hypertensive. The results of the analysis for a given patient can
support doctors in taking pro-active measures for averting the
occurrence of hypertension such as recommendations regarding the
patient behavior in order to lower his hypertension risk. Moreover,
the paper envisages a set of three example scenarios in order to
determine the age when the patient becomes hypertensive, i.e.
determine the threshold for hypertensive age, to analyze what
happens if the threshold hypertensive age is set to a certain age and
the weight of the patient if being varied, and, to set the ideal weight
for the patient and analyze what happens with the threshold of
hypertensive age.
Abstract: A Smart Building Controller (SBC) is a server
software that offers secured access to a pool of building specific
resources, executes monitoring tasks and performs automatic
administration of a building, thus optimizing the exploitation cost and
maximizing comfort. This paper brings to discussion the issues that
arise with the secure exploitation of the SBC administered resources
and proposes a technical solution to implement a robust secure access
system based on roles, individual rights and privileges (special
rights).
Abstract: Data mining idea is mounting rapidly in admiration
and also in their popularity. The foremost aspire of data mining
method is to extract data from a huge data set into several forms that
could be comprehended for additional use. The data mining is a
technology that contains with rich potential resources which could be
supportive for industries and businesses that pay attention to collect
the necessary information of the data to discover their customer’s
performances. For extracting data there are several methods are
available such as Classification, Clustering, Association,
Discovering, and Visualization… etc., which has its individual and
diverse algorithms towards the effort to fit an appropriate model to
the data. STATISTICA mostly deals with excessive groups of data
that imposes vast rigorous computational constraints. These results
trials challenge cause the emergence of powerful STATISTICA Data
Mining technologies. In this survey an overview of the STATISTICA
software is illustrated along with their significant features.
Abstract: Information technology and information systems are
currently at a tipping point. The digital age fundamentally transforms
a large number of industries in the ways they work. Lines between
business and technology blur. Researchers have acknowledged that
this is the time in which the IT/IS organisation needs to re-strategize
itself. In this paper, the author provides a structured review of the IS
and organisation design literature addressing the question of how the
digital age changes the design categories of an IT/IS organisation
design. The findings show that most papers just analyse single
aspects of either IT/IS relevant information or generic organisation
design elements but miss a holistic ‘big-picture’ onto an IT/IS
organisation design. This paper creates a holistic IT/IS organisation
design framework bringing together the IS research strand, the digital
strand and the generic organisation design strand. The research
identified four IT/IS organisation design categories (strategy,
structure, processes and people) and discusses the importance of two
additional categories (sourcing and governance). The authors findings
point to a first anchor point from which further research needs to be
conducted to develop a holistic IT/IS organisation design framework.
Abstract: The formulated problem of optimization of the
technological process of water treatment for thermal power plants is
considered in this article. The problem is of multiparametric nature.
To optimize the process, namely, reduce the amount of waste water, a
new technology was developed to reuse such water. A mathematical
model of the technology of wastewater reuse was developed.
Optimization parameters were determined. The model consists of a
material balance equation, an equation describing the kinetics of ion
exchange for the non-equilibrium case and an equation for the ion
exchange isotherm. The material balance equation includes a
nonlinear term that depends on the kinetics of ion exchange. A direct
problem of calculating the impurity concentration at the outlet of the
water treatment plant was numerically solved. The direct problem
was approximated by an implicit point-to-point computation
difference scheme. The inverse problem was formulated as relates to
determination of the parameters of the mathematical model of the
water treatment plant operating in non-equilibrium conditions. The
formulated inverse problem was solved. Following the results of
calculation the time of start of the filter regeneration process was
determined, as well as the period of regeneration process and the
amount of regeneration and wash water. Multi-parameter
optimization of water treatment process for thermal power plants
allowed decreasing the amount of wastewater by 15%.
Abstract: This paper is a report on the findings of a study
conducted at the Institute of Public Administration (IPA) in Saudi
Arabia. The paper applied both qualitative and quantitative
approaches to assess the levels of basic computer applications’ skills
among students enrolled in the preparatory programs of the
institution. Qualitative data have been collected from semi-structured
interviews with the instructors who have previously been assigned to
teach Introduction to information technology courses. Quantitative
data were collected by executing a self-report questionnaire and a
written statistical test. Three hundred eighty enrolled students
responded to the questionnaire and one hundred forty two
accomplished the statistical test. The results indicate the lack of
necessary skills to deal with computer applications among most of
the students who are enrolled in the IPA’s preparatory programs.
Abstract: In a multi-cultural learning context, where ties are
weak and dynamic, combining qualitative with quantitative research
methods may be more effective. Such a combination may also allow
us to answer different types of question, such as about people’s
perception of the network. In this study the use of observation,
interviews and photos were explored as ways of enhancing data from
social network questionnaires. Integrating all of these methods was
found to enhance the quality of data collected and its accuracy, also
providing a richer story of the network dynamics and the factors that
shaped these changes over time.
Abstract: Nature is the immense gifted source for solving
complex problems. It always helps to find the optimal solution to
solve the problem. Mobile Ad Hoc NETwork (MANET) is a wide
research area of networks which has set of independent nodes. The
characteristics involved in MANET’s are Dynamic, does not depend
on any fixed infrastructure or centralized networks, High mobility.
The Bio-Inspired algorithms are mimics the nature for solving
optimization problems opening a new era in MANET. The typical
Swarm Intelligence (SI) algorithms are Ant Colony Optimization
(ACO), Artificial Bee Colony (ABC), Particle Swarm Optimization
(PSO), Modified Termite Algorithm, Bat Algorithm (BA), Wolf
Search Algorithm (WSA) and so on. This work mainly concentrated
on nature of MANET and behavior of nodes. Also it analyses various
performance metrics such as throughput, QoS and End-to-End delay
etc.
Abstract: The aim of this paper is to understand emerging
learning conditions, when a visual analytics is implemented and used
in K 12 (education). To date, little attention has been paid to the role
visual analytics (digital media and technology that highlight visual
data communication in order to support analytical tasks) can play in
education, and to the extent to which these tools can process
actionable data for young students. This study was conducted in three
public K 12 schools, in four social science classes with students aged
10 to 13 years, over a period of two to four weeks at each school.
Empirical data were generated using video observations and analyzed
with help of metaphors within Actor-network theory (ANT). The
learning conditions are found to be distinguished by broad
complexity, characterized by four dimensions. These emerge from
the actors’ deeply intertwined relations in the activities. The paper
argues in relation to the found dimensions that novel approaches to
teaching and learning could benefit students’ knowledge building as
they work with visual analytics, analyzing visualized data.
Abstract: Cloud computing is a new technology in industry and
academia. The technology has grown and matured in last half decade
and proven their significant role in changing environment of IT
infrastructure where cloud services and resources are offered over the
network. Cloud technology enables users to use services and
resources without being concerned about the technical implications of
technology. There are substantial research work has been performed
for the usage of cloud computing in educational institutes and
majority of them provides cloud services over high-end blade servers
or other high-end CPUs. However, this paper proposes a new stack
called “CiCKAStack” which provide cloud services over unutilized
computing resources, named as commodity computers.
“CiCKAStack” provides IaaS and PaaS using underlying commodity
computers. This will not only increasing the utilization of existing
computing resources but also provide organize file system, on
demand computing resource and design and development
environment.
Abstract: Wireless Sensor Networks (WSNs) have wide variety
of applications and provide limitless future potentials. Nodes in
WSNs are prone to failure due to energy depletion, hardware failure,
communication link errors, malicious attacks, and so on. Therefore,
fault tolerance is one of the critical issues in WSNs. We study how
fault tolerance is addressed in different applications of WSNs. Fault
tolerant routing is a critical task for sensor networks operating in
dynamic environments. Many routing, power management, and data
dissemination protocols have been specifically designed for WSNs
where energy awareness is an essential design issue. The focus,
however, has been given to the routing protocols which might differ
depending on the application and network architecture.
Abstract: In this article we will study the elliptic curve defined
over the ring An and we define the mathematical operations of ECC,
which provides a high security and advantage for wireless
applications compared to other asymmetric key cryptosystem.
Abstract: An artificial neural network is a mathematical model
inspired by biological neural networks. There are several kinds of
neural networks and they are widely used in many areas, such as:
prediction, detection, and classification. Meanwhile, in day to day life,
people always have to make many difficult decisions. For example,
the coach of a soccer club has to decide which offensive player
to be selected to play in a certain game. This work describes a
novel Neural Network using a combination of the General Regression
Neural Network and the Probabilistic Neural Networks to help a
soccer coach make an informed decision.