Abstract: In this paper, we introduce an NLG application for the automatic creation of ready-to-publish texts from big data. The resulting fully automatic generated news stories have a high resemblance to the style in which the human writer would draw up such a story. Topics include soccer games, stock exchange market reports, and weather forecasts. Each generated text is unique. Readyto-publish stories written by a computer application can help humans to quickly grasp the outcomes of big data analyses, save timeconsuming pre-formulations for journalists and cater to rather small audiences by offering stories that would otherwise not exist.
Abstract: STRIM (Statistical Test Rule Induction Method) has been proposed as a method to effectively induct if-then rules from the decision table which is considered as a sample set obtained from the population of interest. Its usefulness has been confirmed by simulation experiments specifying rules in advance, and by comparison with conventional methods. However, scope for future development remains before STRIM can be applied to the analysis of real-world data sets. The first requirement is to determine the size of the dataset needed for inducting true rules, since finding statistically significant rules is the core of the method. The second is to examine the capacity of rule induction from datasets with contaminated attribute values created by missing data and noise, since real-world datasets usually contain such contaminated data. This paper examines the first problem theoretically, in connection with the rule length. The second problem is then examined in a simulation experiment, utilizing the critical size of dataset derived from the first step. The experimental results show that STRIM is highly robust in the analysis of datasets with contaminated attribute values, and hence is applicable to real-world data
Abstract: This paper presents an approach for the classification of
an unstructured format description for identification of file formats.
The main contribution of this work is the employment of data mining
techniques to support file format selection with just the unstructured
text description that comprises the most important format features for
a particular organisation. Subsequently, the file format indentification
method employs file format classifier and associated configurations to
support digital preservation experts with an estimation of required file
format. Our goal is to make use of a format specification knowledge
base aggregated from a different Web sources in order to select file
format for a particular institution. Using the naive Bayes method,
the decision support system recommends to an expert, the file format
for his institution. The proposed methods facilitate the selection of
file format and the quality of a digital preservation process. The
presented approach is meant to facilitate decision making for the
preservation of digital content in libraries and archives using domain
expert knowledge and specifications of file formats. To facilitate
decision-making, the aggregated information about the file formats is
presented as a file format vocabulary that comprises most common
terms that are characteristic for all researched formats. The goal is to
suggest a particular file format based on this vocabulary for analysis
by an expert. The sample file format calculation and the calculation
results including probabilities are presented in the evaluation section.
Abstract: The security of cloud services is the concern of cloud
service providers. In this paper, we will mention different
classifications of cloud attacks referred by specialized organizations.
Each agency has its classification of well-defined properties. The
purpose is to present a high-level classification of current research in
cloud computing security. This classification is organized around
attack strategies and corresponding defenses.
Abstract: Distributed applications deployed on LEO satellites
and ground stations require substantial communication between
different members in a constellation to overcome the earth
coverage barriers imposed by GEOs. Applications running on LEO
constellations suffer the earth line-of-sight blockage effect. They
need adequate lab testing before launching to space. We propose
a scalable cloud-based network simulation framework to simulate
problems created by the earth line-of-sight blockage. The framework
utilized cloud IaaS virtual machines to simulate LEO satellites
and ground stations distributed software. A factorial ANOVA
statistical analysis is conducted to measure simulator overhead on
overall communication performance. The results showed a very low
simulator communication overhead. Consequently, the simulation
framework is proposed as a candidate for testing LEO constellations
with distributed software in the lab before space launch.
Abstract: This paper presents a novel algorithm for secure,
reliable and flexible transmission of big data in two hop wireless
networks using cooperative jamming scheme. Two hop wireless
networks consist of source, relay and destination nodes. Big data has
to transmit from source to relay and from relay to destination by
deploying security in physical layer. Cooperative jamming scheme
determines transmission of big data in more secure manner by
protecting it from eavesdroppers and malicious nodes of unknown
location. The novel algorithm that ensures secure and energy balance
transmission of big data, includes selection of data transmitting
region, segmenting the selected region, determining probability ratio
for each node (capture node, non-capture and eavesdropper node) in
every segment, evaluating the probability using binary based
evaluation. If it is secure transmission resume with the two- hop
transmission of big data, otherwise prevent the attackers by
cooperative jamming scheme and transmit the data in two-hop
transmission.
Abstract: This paper investigates simple implicit force control
algorithms realizable with industrial robots. A lot of approaches
already published are difficult to implement in commercial robot
controllers, because the access to the robot joint torques is necessary
or the complete dynamic model of the manipulator is used. In
the past we already deal with explicit force control of a position
controlled robot. Well known schemes of implicit force control are
stiffness control, damping control and impedance control. Using such
algorithms the contact force cannot be set directly. It is further
the result of controller impedance, environment impedance and
the commanded robot motion/position. The relationships of these
properties are worked out in this paper in detail for the chosen
implicit approaches. They have been adapted to be implementable
on a position controlled robot. The behaviors of stiffness control
and damping control are verified by practical experiments. For this
purpose a suitable test bed was configured. Using the full mechanical
impedance within the controller structure will not be practical in the
case when the robot is in physical contact with the environment. This
fact will be verified by simulation.
Abstract: Thousands of organisations store important and
confidential information related to them, their customers, and their
business partners in databases all across the world. The stored data
ranges from less sensitive (e.g. first name, last name, date of birth) to
more sensitive data (e.g. password, pin code, and credit card
information). Losing data, disclosing confidential information or
even changing the value of data are the severe damages that
Structured Query Language injection (SQLi) attack can cause on a
given database. It is a code injection technique where malicious SQL
statements are inserted into a given SQL database by simply using a
web browser. In this paper, we propose an effective pattern
recognition neural network model for detection and classification of
SQLi attacks. The proposed model is built from three main elements
of: a Uniform Resource Locator (URL) generator in order to generate
thousands of malicious and benign URLs, a URL classifier in order
to: 1) classify each generated URL to either a benign URL or a
malicious URL and 2) classify the malicious URLs into different
SQLi attack categories, and a NN model in order to: 1) detect either a
given URL is a malicious URL or a benign URL and 2) identify the
type of SQLi attack for each malicious URL. The model is first
trained and then evaluated by employing thousands of benign and
malicious URLs. The results of the experiments are presented in
order to demonstrate the effectiveness of the proposed approach.
Abstract: This research study aims to present a retrospective
study about speech recognition systems and artificial intelligence.
Speech recognition has become one of the widely used technologies,
as it offers great opportunity to interact and communicate with
automated machines. Precisely, it can be affirmed that speech
recognition facilitates its users and helps them to perform their daily
routine tasks, in a more convenient and effective manner. This
research intends to present the illustration of recent technological
advancements, which are associated with artificial intelligence.
Recent researches have revealed the fact that speech recognition is
found to be the utmost issue, which affects the decoding of speech. In
order to overcome these issues, different statistical models were
developed by the researchers. Some of the most prominent statistical
models include acoustic model (AM), language model (LM), lexicon
model, and hidden Markov models (HMM). The research will help in
understanding all of these statistical models of speech recognition.
Researchers have also formulated different decoding methods, which
are being utilized for realistic decoding tasks and constrained
artificial languages. These decoding methods include pattern
recognition, acoustic phonetic, and artificial intelligence. It has been
recognized that artificial intelligence is the most efficient and reliable
methods, which are being used in speech recognition.
Abstract: The Orthogonal Frequency Division Multiplexing
(OFDM) with high data rate, high spectral efficiency and its ability to
mitigate the effects of multipath makes them most suitable in wireless
application. Impulsive noise distorts the OFDM transmission and
therefore methods must be investigated to suppress this noise. In this
paper, a State Space Recursive Least Square (SSRLS) algorithm
based adaptive impulsive noise suppressor for OFDM
communication system is proposed. And a comparison with another
adaptive algorithm is conducted. The state space model-dependent
recursive parameters of proposed scheme enables to achieve steady
state mean squared error (MSE), low bit error rate (BER), and faster
convergence than that of some of existing algorithm.
Abstract: In this paper, we propose a system for preventing gas
risks through the use of wireless communication modules and
intelligent gas safety appliances. Our system configuration consists of
an automatic extinguishing system, detectors, a wall-pad, and a
microcomputer controlled micom gas meter to monitor gas flow and
pressure as well as the occurrence of earthquakes. The automatic fire
extinguishing system checks for both combustible gaseous leaks and
monitors the environmental temperature, while the detector array
measures smoke and CO gas concentrations. Depending on detected
conditions, the micom gas meter cuts off an inner valve and generates
a warning, the automatic fire-extinguishing system cuts off an external
valve and sprays extinguishing materials, or the sensors generate
signals and take further action when smoke or CO are detected.
Information on intelligent measures taken by the gas safety appliances
and sensors are transmitted to the wall-pad, which in turn relays this as
real time data to a server that can be monitored via an external network
(BcN) connection to a web or mobile application for the management
of gas safety. To validate this smart-home gas management system, we
field-tested its suitability for use in Korean apartments under several
scenarios.
Abstract: Here, we study the characteristic feature of
conventional (ON-OFF keying) and soliton based transmission
system. We consider 20Gbps transmission system implemented with
Conventional Single Mode Fiber (C-SMF) to examine the role of
Gaussian pulse which is the characteristic of conventional
propagation and Hyperbolic-secant pulse which is the characteristic
of soliton propagation in it. We note the influence of these pulses
with respect to different dispersion lengths and soliton period in
conventional and soliton system respectively and evaluate the system
performance in terms of Quality factor. From the analysis, we could
prove that the soliton pulse has the consistent performance even for
long distance without dispersion compensation than the conventional
system as it is robust to dispersion. For the length of transmission of
200Km, soliton system yielded Q of 33.958 while the conventional
system totally exhausted with Q=0.
Abstract: Cloud computing is the innovative and leading
information technology model for enabling convenient, on-demand
network access to a shared pool of configurable computing resources
that can be rapidly provisioned and released with minimal
management effort. In this paper, we aim at the development of
workflow management system for cloud computing platforms based
on our previous research on the dynamic allocation of the cloud
computing resources and its workflow process. We took advantage of
the HTML5 technology and developed web-based workflow interface.
In order to enable the combination of many tasks running on the cloud
platform in sequence, we designed a mechanism and developed an
execution engine for workflow management on clouds. We also
established a prediction model which was integrated with job queuing
system to estimate the waiting time and cost of the individual tasks on
different computing nodes, therefore helping users achieve maximum
performance at lowest payment. This proposed effort has the potential
to positively provide an efficient, resilience and elastic environment
for cloud computing platform. This development also helps boost user
productivity by promoting a flexible workflow interface that lets users
design and control their tasks' flow from anywhere.
Abstract: We have designed wavelength conversion based on
four wave mixing in an optical fiber at 10 Gb/s. The power of
converted signal increases with increase in signal power. The
converted signal power is investigated as a function of input signal
power and pump power. On comparison of converted signal power at
different value of input signal power, we observe that best converted
signal power is obtained at -2 dBm input signal power for both up
conversion as well as for down conversion. Further, FWM efficiency,
quality factor is observed for increase in input signal power and
optical fiber length.
Abstract: The effects of the pumping wavelength and their power
on the gain flattening of a fiber Raman amplifier (FRA) are
investigated. The multi-wavelength pumping scheme is utilized to
achieve gain flatness in FRA. It is proposed that gain flatness
becomes better with increase in number of pumping wavelengths
applied. We have achieved flat gain with 0.27 dB fluctuation in a
spectral range of 1475-1600 nm for a Raman fiber length of 10 km by
using six pumps with wavelengths with in the 1385-1495 nm interval.
The effect of multi-wavelength pumping scheme on gain saturation in
FRA is also studied. It is proposed that gain saturation condition gets
improved by using this scheme and this scheme is more useful for
higher spans of Raman fiber length.
Abstract: Wavelength Division Multiplexing (WDM) is the dominant transport technology used in numerous high capacity backbone networks, based on optical infrastructures. Given the importance of costs (CapEx and OpEx) associated to these networks, resource management is becoming increasingly important, especially how the optical circuits, called “lightpaths”, are routed throughout the network. This requires the use of efficient algorithms which provide routing strategies with the lowest cost. We focus on the lightpath routing and wavelength assignment problem, known as the RWA problem, while optimizing wavelength fragmentation over the network. Wavelength fragmentation poses a serious challenge for network operators since it leads to the misuse of the wavelength spectrum, and then to the refusal of new lightpath requests. In this paper, we first establish a new Integer Linear Program (ILP) for the problem based on a node-link formulation. This formulation is based on a multilayer approach where the original network is decomposed into several network layers, each corresponding to a wavelength. Furthermore, we propose an efficient heuristic for the problem based on a greedy algorithm followed by a post-treatment procedure. The obtained results show that the optimal solution is often reached. We also compare our results with those of other RWA heuristic methods
Abstract: The article deals with the tool in Matlab GUI form
that is designed to analyse a mechatronic system sensitivity and
tolerance. In the analysed mechatronic system, a torque is transferred
from the drive to the load through a coupling containing flexible
elements. Different methods of control system design are used. The
classic form of the feedback control is proposed using Naslin method,
modulus optimum criterion and inverse dynamics method. The
cascade form of the control is proposed based on combination of
modulus optimum criterion and symmetric optimum criterion. The
sensitivity is analysed on the basis of absolute and relative sensitivity
of system function to the change of chosen parameter value of the
mechatronic system, as well as the control subsystem. The tolerance
is analysed in the form of determining the range of allowed relative
changes of selected system parameters in the field of system stability.
The tool allows to analyse an influence of torsion stiffness, torsion
damping, inertia moments of the motor and the load and controller(s)
parameters. The sensitivity and tolerance are monitored in terms of
the impact of parameter change on the response in the form of system
step response and system frequency-response logarithmic
characteristics. The Symbolic Math Toolbox for expression of the
final shape of analysed system functions was used. The sensitivity
and tolerance are graphically represented as 2D graph of sensitivity
or tolerance of the system function and 3D/2D static/interactive graph
of step/frequency response.
Abstract: This paper is concerned with knowledge representation
and extraction of fuzzy if-then rules using Interval Type-2
Context-based Fuzzy C-Means clustering (IT2-CFCM) with the aid of
fuzzy granulation. This proposed clustering algorithm is based on
information granulation in the form of IT2 based Fuzzy C-Means
(IT2-FCM) clustering and estimates the cluster centers by preserving
the homogeneity between the clustered patterns from the IT2 contexts
produced in the output space. Furthermore, we can obtain the
automatic knowledge representation in the design of Radial Basis
Function Networks (RBFN), Linguistic Model (LM), and Adaptive
Neuro-Fuzzy Networks (ANFN) from the numerical input-output data
pairs. We shall focus on a design of ANFN in this paper. The
experimental results on an estimation problem of energy performance
reveal that the proposed method showed a good knowledge
representation and performance in comparison with the previous
works.
Abstract: Typically, virtual communities exhibit the well-known
phenomenon of participation inequality, which means that only a
small percentage of users is responsible of the majority of
contributions. However, the sustainability of the community requires
that the group of active users must be continuously nurtured with new
users that gain expertise through a participation process. This paper
analyzes the time evolution of Open Source Software (OSS)
communities, considering users that join/abandon the community
over time and several topological properties of the network when
modeled as a social network. More specifically, the paper analyzes
the role of those users rejoining the community and their influence in
the global characteristics of the network.
Abstract: Electronic Word-Of-Mouth (eWOM) communities
represent today an important source of information in which more
and more customers base their purchasing decisions. They include
thousands of reviews concerning very different products and services
posted by many individuals geographically distributed all over the
world. Due to their massive audience, eWOM communities can help
users to find the product they are looking for even if they are less
popular or rare. This is known as the long tail effect, which leads to a
larger number of lower-selling niche products. This paper analyzes
the long tail effect in a well-known eWOM community and defines a
tool for finding niche products unavailable through conventional
channels.