Abstract: Group key management is an important functional
building block for any secure multicast architecture.
Thereby, it has been extensively studied in the literature.
In this paper we present relevant group key management
protocols. Then, we compare them against some pertinent
performance criteria.
Abstract: The effects of commercial or bovine yeasts on the
performance and blood variables of broiler chickens intoxicated with
aflatoxin were investigated in broilers. Four hundred eighty broilers
(Arbor Acres; 3-wk-old) were randomly assigned to 4 groups. Each
group (120 broiler chickens) was further randomly divided into 6
replicates of 20 chickens. The treatments were control diet without
additives (treatment 1), 250 ppb AFB1 (treatment 2), commercial
yeast, Saccharomyces cerevisiae, (CY 2.5 x 107 CFU/g) + 250 ppb
AFB1 (treatment 3) and bovine yeast, Saccharomyces cerevisiae,
(BY 2.5 x 107 CFU/g + 250 ppb AFB1 (treatment 4). Complete
randomized design (CRD) was used in the experiment. Feed
consumption and body weight were recorded at every five-day
period. On day 42, carcass compositions were determined from 30
birds per treatment. While chicks were sacrificed, 3-4 ml blood
sample was taken and stored frozen at (-20°C) for serum chemical
analysis to determine effects of consumption of diets on blood
chemistry (total protein, albumin, glucose, urea, cholesterol and
triglycerides). There were no significant differences in ADFI among
the treatments(P>0.05). However, BWG, FCR and mortality were
highly significantly different (P
Abstract: Human Resource (HR) applications can be used to
provide fair and consistent decisions, and to improve the
effectiveness of decision making processes. Besides that, among
the challenge for HR professionals is to manage organization
talents, especially to ensure the right person for the right job at the
right time. For that reason, in this article, we attempt to describe
the potential to implement one of the talent management tasks i.e.
identifying existing talent by predicting their performance as one of
HR application for talent management. This study suggests the
potential HR system architecture for talent forecasting by using
past experience knowledge known as Knowledge Discovery in
Database (KDD) or Data Mining. This article consists of three
main parts; the first part deals with the overview of HR
applications, the prediction techniques and application, the general
view of Data mining and the basic concept of talent management
in HRM. The second part is to understand the use of Data Mining
technique in order to solve one of the talent management tasks, and
the third part is to propose the potential HR system architecture for
talent forecasting.
Abstract: we propose a new normalized LMS (NLMS) algorithm, which gives satisfactory performance in certain applications in comaprison with con-ventional NLMS recursion. This new algorithm can be treated as a block based simplification of NLMS algorithm with significantly reduced number of multi¬ply and accumulate as well as division operations. It is also shown that such a recursion can be easily implemented in block floating point (BFP) arithmetic, treating the implementational issues much efficiently. In particular, the core challenges of a BFP realization to such adaptive filters are mainly considered in this regard. A global upper bound on the step size control parameter of the new algorithm due to BFP implementation is also proposed to prevent overflow in filtering as well as weight updating operations jointly.
Abstract: The compressive strength development through
polymerization process of alkaline solution and fly ash blended with Microwave Incinerated Rice Husk Ash (MIRHA) is described in this
paper. Three curing conditions, which are hot gunny curing, ambient
curing, and external humidity curing are investigated to obtain the suitable curing condition for cast in situ provision. Fly ash was
blended with MIRHA at 3%, 5%, and 7% to identify the effect of blended mixes to the compressive strength and microstructure
properties of geopolymer concrete. Compressive strength results
indicated an improvement in the strength development with external humidity curing concrete samples compared to hot gunny curing and
ambient curing. Blended mixes also presented better performance
than control mixes. Improvement of interfacial transition zone (ITZ)
and micro structure in external humidity concrete samples were also
identified compared to hot gunny and ambient curing.
Abstract: Low-density parity-check (LDPC) codes have been shown to deliver capacity approaching performance; however, problematic graphical structures (e.g. trapping sets) in the Tanner graph of some LDPC codes can cause high error floors in bit-error-ratio (BER) performance under conventional sum-product algorithm (SPA). This paper presents a serial concatenation scheme to avoid the trapping sets and to lower the error floors of LDPC code. The outer code in the proposed concatenation is the LDPC, and the inner code is a high rate array code. This approach applies an interactive hybrid process between the BCJR decoding for the array code and the SPA for the LDPC code together with bit-pinning and bit-flipping techniques. Margulis code of size (2640, 1320) has been used for the simulation and it has been shown that the proposed concatenation and decoding scheme can considerably improve the error floor performance with minimal rate loss.
Abstract: Service innovations are central concerns in fast
changing environment. Due to the fitness in customer demands and
advances in information technologies (IT) in service management, an
expanded conceptualization of e-service innovation is required.
Specially, innovation practices have become increasingly more
challenging, driving managers to employ a different open innovation
model to maintain competitive advantages. At the same time, firms
need to interact with external and internal customers in innovative
environments, like the open innovation networks, to co-create values.
Based on these issues, an important conceptual framework of e-service
innovation is developed. This paper aims to examine the contributing
factors on e-service innovation and firm performance, including
financial and non-financial aspects. The study concludes by showing
how e-service innovation will play a significant role in growing the
overall values of the firm. The discussion and conclusion will lead to a
stronger understanding of e-service innovation and co-creating values
with customers within open innovation networks.
Abstract: A study was carried out to determine the effect of water quality on flotation performance. The experimental test work comprised of batch flotation tests using Denver lab cell for a period of 10 minutes. Nine different test runs were carried out in triplicates to ensure reproducibility using different water types from different thickener overflows, return and sewage effluent water (process water) and portable water. The water sources differed in pH, total dissolved solids, total suspended solids and conductivity. Process water was found to reduce the concentrate recovery and mass pull, while portable water increased the concentrate recovery and mass pull. Portable water reduced the concentrate grade while process water increased the concentrate grade. It is proposed that a combination of process water and portable water supply be used in flotation circuits to balance the different effects that the different water types have on the flotation efficiency.
Abstract: The performance of an image filtering system depends
on its ability to detect the presence of noisy pixels in the image. Most
of the impulse detection schemes assume the presence of salt and
pepper noise in the images and do not work satisfactorily in case of
uniformly distributed impulse noise. In this paper, a new algorithm is
presented to improve the performance of switching median filter in
detection of uniformly distributed impulse noise. The performance of
the proposed scheme is demonstrated by the results obtained from
computer simulations on various images.
Abstract: In illumination variant face recognition, existing
methods extracting face albedo as light normalized image may lead to
loss of extensive facial details, with light template discarded. To
improve that, a novel approach for realistic facial texture
reconstruction by combining original image and albedo image is
proposed. First, light subspaces of different identities are established
from the given reference face images; then by projecting the original
and albedo image into each light subspace respectively, texture
reference images with corresponding lighting are reconstructed and
two texture subspaces are formed. According to the projections in
texture subspaces, facial texture with normal light can be synthesized.
Due to the combination of original image, facial details can be
preserved with face albedo. In addition, image partition is applied to
improve the synthesization performance. Experiments on Yale B and
CMUPIE databases demonstrate that this algorithm outperforms the
others both in image representation and in face recognition.
Abstract: In many applications, it is a priori known that the
target function should satisfy certain constraints imposed by, for
example, economic theory or a human-decision maker. Here we
consider partially monotone problems, where the target variable
depends monotonically on some of the predictor variables but not all.
We propose an approach to build partially monotone models based
on the convolution of monotone neural networks and kernel
functions. The results from simulations and a real case study on
house pricing show that our approach has significantly better
performance than partially monotone linear models. Furthermore, the
incorporation of partial monotonicity constraints not only leads to
models that are in accordance with the decision maker's expertise,
but also reduces considerably the model variance in comparison to
standard neural networks with weight decay.
Abstract: The effect of calcination temperature and MgO crystallite sizes on the structure and catalytic performance of TiO2 supported nano-MgO catalyst for the trans-esterification of soybean oil has been studied. The catalyst has been prepared by deposition precipitation method, characterised by XRD and FTIR and tested in an autoclave at 225oC. The soybean oil conversion after 15 minutes of the trans-esterification reaction increased when the calcination temperature was increased from 500 to 600oC and decreased with further increase in calcination temperature. Some glycerolysis activity was also detected on catalysts calcined at 600 and 700oC after 45 minutes of reaction. The trans-esterification reaction rate increased with the decrease in MgO crystallite size for the first 30 min.
Abstract: Various models have been derived by studying large number of completed software projects from various organizations and applications to explore how project sizes mapped into project effort. But, still there is a need to prediction accuracy of the models. As Neuro-fuzzy based system is able to approximate the non-linear function with more precision. So, Neuro-Fuzzy system is used as a soft computing approach to generate model by formulating the relationship based on its training. In this paper, Neuro-Fuzzy technique is used for software estimation modeling of on NASA software project data and performance of the developed models are compared with the Halstead, Walston-Felix, Bailey-Basili and Doty Models mentioned in the literature.
Abstract: I/O workload is a critical and important factor to
analyze I/O pattern and to maximize file system performance.
However to measure I/O workload on running distributed parallel file
system is non-trivial due to collection overhead and large volume of
data. In this paper, we measured and analyzed file system activities on
two large-scale cluster systems which had TFlops level high
performance computation resources. By comparing file system
activities of 2009 with those of 2006, we analyzed the change of I/O
workloads by the development of system performance and high-speed
network technology.
Abstract: The backpropagation algorithm in general employs quadratic error function. In fact, most of the problems that involve minimization employ the Quadratic error function. With alternative error functions the performance of the optimization scheme can be improved. The new error functions help in suppressing the ill-effects of the outliers and have shown good performance to noise. In this paper we have tried to evaluate and compare the relative performance of complex valued neural network using different error functions. During first simulation for complex XOR gate it is observed that some error functions like Absolute error, Cauchy error function can replace Quadratic error function. In the second simulation it is observed that for some error functions the performance of the complex valued neural network depends on the architecture of the network whereas with few other error functions convergence speed of the network is independent of architecture of the neural network.
Abstract: The background estimation approach using a small
window median filter is presented on the bases of analyzing IR point
target, noise and clutter model. After simplifying the two-dimensional
filter, a simple method of adopting one-dimensional median filter is
illustrated to make estimations of background according to the
characteristics of IR scanning system. The adaptive threshold is used
to segment canceled image in the background. Experimental results
show that the algorithm achieved good performance and satisfy the
requirement of big size image-s real-time processing.
Abstract: The problem of frequent pattern discovery is defined
as the process of searching for patterns such as sets of features or items that appear in data frequently. Finding such frequent patterns
has become an important data mining task because it reveals associations, correlations, and many other interesting relationships
hidden in a database. Most of the proposed frequent pattern mining
algorithms have been implemented with imperative programming
languages. Such paradigm is inefficient when set of patterns is large
and the frequent pattern is long. We suggest a high-level declarative
style of programming apply to the problem of frequent pattern
discovery. We consider two languages: Haskell and Prolog. Our
intuitive idea is that the problem of finding frequent patterns should
be efficiently and concisely implemented via a declarative paradigm
since pattern matching is a fundamental feature supported by most
functional languages and Prolog. Our frequent pattern mining
implementation using the Haskell and Prolog languages confirms our
hypothesis about conciseness of the program. The comparative
performance studies on line-of-code, speed and memory usage of
declarative versus imperative programming have been reported in the
paper.
Abstract: Cu-mesoporous TiO2 is developed for removal acid
odor cooperated with ozone assistance and online- regeneration
system with/without UV irradiation (all weather) in study. The results
showed that Cu-mesoporous TiO2 present the desirable adsorption
efficiency of acid odor without UV irradiation, due to the larger
surface area, pore sizeand the additional absorption ability provided by
Cu. In the photocatalysis process, the material structure also benefits
Cu-mesoporous TiO2 to perform the more outstanding efficiency on
degrading acid odor. Cu also postponed the recombination of
electron-hole pairs excited from TiO2 to enhance photodegradation
ability. Cu-mesoporous TiO2 could gain the conspicuous increase on
photocatalysis ability from ozone assistance, but without any benefit
on adsorption. In addition, the online regeneration procedure could
process the used Cu-mesoporous TiO2 to reinstate the adsorption
ability and maintain the photodegradtion performance, depended on
scrubbing, desorping acid odor and reducing Cu to metal state.
Abstract: This paper presents the design, analysis and
development of permanent magnet (PM) torque couplers. These
couplers employ rare-earth magnets. Based on finite element analysis
and earlier analytical works both concentric and face-type
synchronous type couplers have been designed and fabricated. The
experimental performance has good correlation with finite element
calculations.
Abstract: Given a parallel program to be executed on a heterogeneous
computing system, the overall execution time of the program
is determined by a schedule. In this paper, we analyze the worst-case
performance of the list scheduling algorithm for scheduling tasks
of a parallel program in a mixed-machine heterogeneous computing
system such that the total execution time of the program is minimized.
We prove tight lower and upper bounds for the worst-case
performance ratio of the list scheduling algorithm. We also examine
the average-case performance of the list scheduling algorithm. Our
experimental data reveal that the average-case performance of the list
scheduling algorithm is much better than the worst-case performance
and is very close to optimal, except for large systems with large
heterogeneity. Thus, the list scheduling algorithm is very useful in
real applications.