Abstract: The proliferation of user-generated content (UGC) results in huge opportunities to explore event patterns. However, existing event recommendation systems primarily focus on advanced information technology users. Little work has been done to address novice and low-literacy users. The next billion users providing and consuming UGC are likely to include communities from developing countries who are ready to use affordable technologies for subsistence goals. Therefore, we propose a design framework for providing event recommendations to address the needs of such users. Grounded in information integration theory (IIT), our framework advocates that effective event recommendation is supported by systems capable of (1) reliable information gathering through structured user input, (2) accurate sense making through spatial-temporal analytics, and (3) intuitive information dissemination through interactive visualization techniques. A mobile pest management application is developed as an instantiation of the design framework. Our preliminary study suggests a set of design principles for novice and low-literacy users.
Abstract: The transition to sustainable development requires
considerable investments from stakeholders, both financial and
immaterial. However, accounting for such investments often poses a
challenge, as ventures with intangible or non-financial returns remain
oblivious to conventional accounting techniques and risk assessment.
That such investments may significantly contribute to the welfare of
those affected may act as a driving force behind attempting to bridge
this gap. This gains crucial importance as investments must be also
backed by governments and administrations; entities whose budget
depends on taxpayers- contributions and whose tasks are based on
securing the welfare of their citizens. Besides economic welfare,
citizens also require social and environmental wellbeing too.
However, administrations must also safeguard that welfare is
guaranteed not only to present, but to future generations too. With
already strained budgets and the requirement of sustainable
development, governments on all levels face the double challenge of
making both of these ends meet.
Abstract: Biclustering is a very useful data mining technique for
identifying patterns where different genes are co-related based on a
subset of conditions in gene expression analysis. Association rules
mining is an efficient approach to achieve biclustering as in
BIMODULE algorithm but it is sensitive to the value given to its
input parameters and the discretization procedure used in the
preprocessing step, also when noise is present, classical association
rules miners discover multiple small fragments of the true bicluster,
but miss the true bicluster itself. This paper formally presents a
generalized noise tolerant bicluster model, termed as μBicluster. An
iterative algorithm termed as BIDENS based on the proposed model
is introduced that can discover a set of k possibly overlapping
biclusters simultaneously. Our model uses a more flexible method to
partition the dimensions to preserve meaningful and significant
biclusters. The proposed algorithm allows discovering biclusters that
hard to be discovered by BIMODULE. Experimental study on yeast,
human gene expression data and several artificial datasets shows that
our algorithm offers substantial improvements over several
previously proposed biclustering algorithms.
Abstract: The effect of chemical treatment in CdCl2 on the
compositional changes and defect structures of potentially useful ZnS
solar cell thin films prepared by vacuum deposition method was
studied using the complementary Rutherford backscattering (RBS)
and Thermoluminesence (TL) techniques. A series of electron and
hole traps are found in the various as deposited samples studied.
After treatment, perturbation on the intensity is noted; mobile defect
states and charge conversion and/or transfer between defect states are
found.
Abstract: An on-demand routing protocol for wireless ad hoc
networks is one that searches for and attempts to discover a route to
some destination node only when a sending node originates a data
packet addressed to that node. In order to avoid the need for such a
route discovery to be performed before each data packet is sent, such
routing protocols must cache routes previously discovered. This
paper presents an analysis of the effect of intelligent caching in a non
clustered network, using on-demand routing protocols in wireless ad
hoc networks. The analysis carried out is based on the Dynamic
Source Routing protocol (DSR), which operates entirely on-demand.
DSR uses the cache in every node to save the paths that are learnt
during route discovery procedure. In this implementation, caching
these paths only at intermediate nodes and using the paths from these
caches when required is tried. This technique helps in storing more
number of routes that are learnt without erasing the entries in the
cache, to store a new route that is learnt.
The simulation results on DSR have shown that this technique
drastically increases the available memory for caching the routes
discovered without affecting the performance of the DSR routing
protocol in any way, except for a small increase in end to end delay.
Abstract: Insulation used in transformer is mostly oil pressboard insulation. Insulation failure is one of the major causes of catastrophic failure of transformers. It is established that partial discharges (PD) cause insulation degradation and premature failure of insulation. Online monitoring of PDs can reduce the risk of catastrophic failure of transformers. There are different techniques of partial discharge measurement like, electrical, optical, acoustic, opto-acoustic and ultra high frequency (UHF). Being non invasive and non interference prone, acoustic emission technique is advantageous for online PD measurement. Acoustic detection of p.d. is based on the retrieval and analysis of mechanical or pressure signals produced by partial discharges. Partial discharges are classified according to the origin of discharges. Their effects on insulation deterioration are different for different types. This paper reports experimental results and analysis for classification of partial discharges using acoustic emission signal of laboratory simulated partial discharges in oil pressboard insulation system using three different electrode systems. Acoustic emission signal produced by PD are detected by sensors mounted on the experimental tank surface, stored on an oscilloscope and fed to computer for further analysis. The measured AE signals are analyzed using discrete wavelet transform analysis and wavelet packet analysis. Energy distribution in different frequency bands of discrete wavelet decomposed signal and wavelet packet decomposed signal is calculated. These analyses show a distinct feature useful for PD classification. Wavelet packet analysis can sort out any misclassification arising out of DWT in most cases.
Abstract: Cardiac pulse-related artifacts in the EEG recorded
simultaneously with fMRI are complex and highly variable. Their
effective removal is an unsolved problem. Our aim is to develop an
adaptive removal algorithm based on the matching pursuit (MP)
technique and to compare it to established methods using a visual
evoked potential (VEP). We recorded the VEP inside the static
magnetic field of an MR scanner (with artifacts) as well as in an
electrically shielded room (artifact free). The MP-based artifact
removal outperformed average artifact subtraction (AAS) and
optimal basis set removal (OBS) in terms of restoring the EEG field
map topography of the VEP. Subsequently, a dipole model was fitted
to the VEP under each condition using a realistic boundary element
head model. The source location of the VEP recorded inside the MR
scanner was closest to that of the artifact free VEP after cleaning
with the MP-based algorithm as well as with AAS. While none of the
tested algorithms offered complete removal, MP showed promising
results due to its ability to adapt to variations of latency, frequency
and amplitude of individual artifact occurrences while still utilizing a
common template.
Abstract: In this paper, an ultrasonic technique is proposed to
predict oil content in a fresh palm fruit. This is accomplished by
measuring the attenuation based on ultrasonic transmission mode.
Several palm fruit samples with known oil content by Soxhlet
extraction (ISO9001:2008) were tested with our ultrasonic
measurement. Amplitude attenuation data results for all palm samples
were collected. The Feedforward Neural Networks (FNNs) are
applied to predict the oil content for the samples. The Root Mean
Square Error (RMSE) and Mean Absolute Error (MAE) of the FNN
model for predicting oil content percentage are 7.6186 and 5.2287
with the correlation coefficient (R) of 0.9193.
Abstract: This paper presents an optimized algorithm for robot localization which increases the correctness and accuracy of the estimating position of mobile robot to more than 150% of the past methods [1] in the uncertain and noisy environment. In this method the odometry and vision sensors are combined by an adapted well-known discrete kalman filter [2]. This technique also decreased the computation process of the algorithm by DKF simple implementation. The experimental trial of the algorithm is performed on the robocup middle size soccer robot; the system can be used in more general environments.
Abstract: In this work, we present a reliable framework to solve boundary value problems with particular significance in solid mechanics. These problems are used as mathematical models in deformation of beams. The algorithm rests mainly on a relatively new technique, the Variational Iteration Method. Some examples are given to confirm the efficiency and the accuracy of the method.
Abstract: Copper based composites reinforced with WC and Ti
particles were prepared using planetary ball-mill. The experiment
was designed by using Taguchi technique and milling was carried out
in an air for several hours. The powder was characterized before and
after milling using the SEM, TEM and X-ray for microstructure and
for possible new phases. Microstructures show that milled particles
size and reduction in particle size depend on many parameters. The
distance d between planes of atoms estimated from X-ray powder
diffraction data and TEM image. X-ray diffraction patterns of the
milled powder did not show clearly any new peak or energy shift, but
the TEM images show a significant change in crystalline structure of
corporate on titanium in the composites.
Abstract: Alcohol and water extracts of Cymbopogon citratus
was investigated for anti-bacterial properties and phytochemical
constituents. The extract was screened against four gram-negative
bacteria Escherichia coli, Klebsiella pneumoniae, Pseudomonas
aeruginosa, Proteus vulgaris) and two grampositive bacteria Bacillus
subtilis and Staphylococcus aureus at four different concentrations
(1:1, 1:5, 1:10 and 1:20) using disc diffusion method. The antibacterial
examination was by disc diffusion techniques, while the
photochemical constituents were investigated using standard
chemical methods. Results showed that the extracts inhibited the
growth of standard and local strains of the organisms used. The
treatments were significantly different (P = 0.05). The minimum
inhibitory concentration of the extracts against the tested
microorganisms ranged between 150mg/ml and 50mg/ml. The
alcohol extracts were found to be generally more effective than the
water extract. The photochemical analysis revealed the presence of
alkaloids and phenol but absence of cardiac and cyanogenic
glycosides. The presence of alkaloid and phenols were inferred as
being responsible for the anti-bacterial properties of the extracts.
Abstract: Dynamics of laser radiation – metal target interaction
in water at 1064 nm by applying Mach-Zehnder interference
technique was studied. The mechanism of generating the well
developed regime of evaporation of a metal surface and a spherical
shock wave in water is proposed. Critical intensities of the NIR for
the well developed evaporation of silver and gold targets were
determined. Dynamics of shock waves was investigated for earlier
(dozens) and later (hundreds) nanoseconds of time. Transparent
expanding plasma-vapor-compressed water object was visualized and
measured. The thickness of compressed layer of water and pressures
behind the front of a shock wave for later time delays were obtained
from the optical treatment of interferograms.
Abstract: The steady mixed convection boundary layer flow from
a vertical cone in a porous medium filled with a nanofluid is
numerically investigated using different types of nanoparticles as Cu
(copper), Al2O3 (alumina) and TiO2 (titania). The boundary value
problem is solved by using the shooting technique by reducing it
into an ordinary differential equation. Results of interest for the local
Nusselt number with various values of the constant mixed convection
parameter and nanoparticle volume fraction parameter are evaluated.
It is found that dual solutions exist for a certain range of mixed
convection parameter.
Abstract: Software project effort estimation is frequently seen
as complex and expensive for individual software engineers.
Software production is in a crisis. It suffers from excessive costs.
Software production is often out of control. It has been suggested that
software production is out of control because we do not measure.
You cannot control what you cannot measure. During last decade, a
number of researches on cost estimation have been conducted. The
metric-set selection has a vital role in software cost estimation
studies; its importance has been ignored especially in neural network
based studies. In this study we have explored the reasons of those
disappointing results and implemented different neural network
models using augmented new metrics. The results obtained are
compared with previous studies using traditional metrics. To be able
to make comparisons, two types of data have been used. The first
part of the data is taken from the Constructive Cost Model
(COCOMO'81) which is commonly used in previous studies and the
second part is collected according to new metrics in a leading
international company in Turkey. The accuracy of the selected
metrics and the data samples are verified using statistical techniques.
The model presented here is based on Multi-Layer Perceptron
(MLP). Another difficulty associated with the cost estimation studies
is the fact that the data collection requires time and care. To make a
more thorough use of the samples collected, k-fold, cross validation
method is also implemented. It is concluded that, as long as an
accurate and quantifiable set of metrics are defined and measured
correctly, neural networks can be applied in software cost estimation
studies with success
Abstract: There are various overlay structures that provide
efficient and scalable solutions for point and range query in a peer-topeer
network. Overlay structure based on m-Binary Search Tree
(BST) is one such popular technique. It deals with the division of the
tree into different key intervals and then assigning the key intervals to
a BST. The popularity of the BST makes this overlay structure
vulnerable to different kinds of attacks. Here we present four such
possible attacks namely index poisoning attack, eclipse attack,
pollution attack and syn flooding attack. The functionality of BST is
affected by these attacks. We also provide different security
techniques that can be applied against these attacks.
Abstract: Clustering is the process of subdividing an input data set into a desired number of subgroups so that members of the same subgroup are similar and members of different subgroups have diverse properties. Many heuristic algorithms have been applied to the clustering problem, which is known to be NP Hard. Genetic algorithms have been used in a wide variety of fields to perform clustering, however, the technique normally has a long running time in terms of input set size. This paper proposes an efficient genetic algorithm for clustering on very large data sets, especially on image data sets. The genetic algorithm uses the most time efficient techniques along with preprocessing of the input data set. We test our algorithm on both artificial and real image data sets, both of which are of large size. The experimental results show that our algorithm outperforms the k-means algorithm in terms of running time as well as the quality of the clustering.
Abstract: Intrusion Detection System is significant in network
security. It detects and identifies intrusion behavior or intrusion
attempts in a computer system by monitoring and analyzing the
network packets in real time. In the recent year, intelligent algorithms
applied in the intrusion detection system (IDS) have been an
increasing concern with the rapid growth of the network security.
IDS data deals with a huge amount of data which contains irrelevant
and redundant features causing slow training and testing process,
higher resource consumption as well as poor detection rate. Since the
amount of audit data that an IDS needs to examine is very large even
for a small network, classification by hand is impossible. Hence, the
primary objective of this review is to review the techniques prior to
classification process suit to IDS data.
Abstract: Robot manipulators are highly coupled nonlinear
systems, therefore real system and mathematical model of dynamics
used for control system design are not same. Hence, fine-tuning of
controller is always needed. For better tuning fast simulation speed
is desired. Since, Matlab incorporates LAPACK to increase the speed
and complexity of matrix computation, dynamics, forward and
inverse kinematics of PUMA 560 is modeled on Matlab/Simulink in
such a way that all operations are matrix based which give very less
simulation time. This paper compares PID parameter tuning using
Genetic Algorithm, Simulated Annealing, Generalized Pattern Search
(GPS) and Hybrid Search techniques. Controller performances for all
these methods are compared in terms of joint space ITSE and
cartesian space ISE for tracking circular and butterfly trajectories.
Disturbance signal is added to check robustness of controller. GAGPS
hybrid search technique is showing best results for tuning PID
controller parameters in terms of ITSE and robustness.
Abstract: The design of distributed systems involves the
partitioning of the system into components or partitions and the
allocation of these components to physical nodes. Techniques have
been proposed for both the partitioning and allocation process.
However these techniques suffer from a number of limitations. For
instance object replication has the potential to greatly improve the
performance of an object orientated distributed system but can be
difficult to use effectively and there are few techniques that support
the developer in harnessing object replication.
This paper presents a methodological technique that helps
developers decide how objects should be allocated in order to
improve performance in a distributed system that supports
replication. The performance of the proposed technique is
demonstrated and tested on an example system.