Abstract: The purpose of this study was to develop a “teachers’
self-efficacy scale for high school physical education teachers
(TSES-HSPET)” in Taiwan. This scale is based on the self-efficacy
theory of Bandura [1], [2]. This study used exploratory and
confirmatory factor analyses to test the reliability and validity. The
participants were high school physical education teachers in Taiwan.
Both stratified random sampling and cluster sampling were used to
sample participants for the study. 350 teachers were sampled in the
first stage and 234 valid scales (male 133, female 101) returned.
During the second stage, 350 teachers were sampled and 257 valid
scales (male 143, female 110, 4 did not indicate gender) returned. The
exploratory factor analysis was used in the first stage, and it got
60.77% of total variance for construct validity. The Cronbach’s alpha
coefficient of internal consistency was 0.91 for sumscale, and
subscales were 0.84 and 0.90. In the second stage, confirmatory factor
analysis was used to test construct validity. The result showed that the
fit index could be accepted (χ2 (75) =167.94, p
Abstract: Understanding the cell's large-scale organization is an
interesting task in computational biology. Thus, protein-protein
interactions can reveal important organization and function of the
cell. Here, we investigated the correspondence between protein
interactions and function for the yeast. We obtained the correlations
among the set of proteins. Then these correlations are clustered using
both the hierarchical and biclustering methods. The detailed analyses
of proteins in each cluster were carried out by making use of their
functional annotations. As a result, we found that some functional
classes appear together in almost all biclusters. On the other hand, in
hierarchical clustering, the dominancy of one functional class is
observed. In brief, from interaction data to function, some correlated
results are noticed about the relationship between interaction and
function which might give clues about the organization of the
proteins.
Abstract: The main goal of microarray experiments is to quantify the expression of every object on a slide as precisely as possible, with a further goal of clustering the objects. Recently, many studies have discussed clustering issues involving similar patterns of gene expression. This paper presents an application of fuzzy-type methods for clustering DNA microarray data that can be applied to typical comparisons. Clustering and analyses were performed on microarray and simulated data. The results show that fuzzy-possibility c-means clustering substantially improves the findings obtained by others.
Abstract: Many experimental results suggest that more precise
spike timing is significant in neural information processing. We
construct a self-organization model using the spatiotemporal patterns,
where Spike-Timing Dependent Plasticity (STDP) tunes the
conduction delays between neurons. We show that the fluctuation of
conduction delays causes globally continuous and locally distributed
firing patterns through the self-organization.
Abstract: In this paper, we present a new and effective image indexing technique that extracts features directly from DCT domain. Our proposed approach is an object-based image indexing. For each block of size 8*8 in DCT domain a feature vector is extracted. Then, feature vectors of all blocks of image using a k-means algorithm is clustered into groups. Each cluster represents a special object of the image. Then we select some clusters that have largest members after clustering. The centroids of the selected clusters are taken as image feature vectors and indexed into the database. Also, we propose an approach for using of proposed image indexing method in automatic image classification. Experimental results on a database of 800 images from 8 semantic groups in automatic image classification are reported.
Abstract: The prevalence of non organic constipation differs
from country to country and the reliability of the estimate rates is
uncertain. Moreover, the clinical relevance of subdividing the
heterogeneous functional constipation disorders into pre-defined
subgroups is largely unknown.. Aim: to estimate the prevalence of
constipation in a population-based sample and determine whether
clinical subgroups can be identified. An age and gender stratified
sample population from 5 Italian cities was evaluated using a
previously validated questionnaire. Data mining by cluster analysis
was used to determine constipation subgroups. Results: 1,500
complete interviews were obtained from 2,083 contacted households
(72%). Self-reported constipation correlated poorly with symptombased
constipation found in 496 subjects (33.1%). Cluster analysis
identified four constipation subgroups which correlated to subgroups
identified according to pre-defined symptom criteria. Significant
differences in socio-demographics and lifestyle were observed
among subgroups.
Abstract: Scheduling of diversified service requests in
distributed computing is a critical design issue. Cloud is a type of
parallel and distributed system consisting of a collection of
interconnected and virtual computers. It is not only the clusters and
grid but also it comprises of next generation data centers. The paper
proposes an initial heuristic algorithm to apply modified ant colony
optimization approach for the diversified service allocation and
scheduling mechanism in cloud paradigm. The proposed optimization
method is aimed to minimize the scheduling throughput to service all
the diversified requests according to the different resource allocator
available under cloud computing environment.
Abstract: In the present work, we propose a new technique to
enhance the learning capabilities and reduce the computation
intensity of a competitive learning multi-layered neural network
using the K-means clustering algorithm. The proposed model use
multi-layered network architecture with a back propagation learning
mechanism. The K-means algorithm is first applied to the training
dataset to reduce the amount of samples to be presented to the neural
network, by automatically selecting an optimal set of samples. The
obtained results demonstrate that the proposed technique performs
exceptionally in terms of both accuracy and computation time when
applied to the KDD99 dataset compared to a standard learning
schema that use the full dataset.
Abstract: The multiple traveling salesman problem (mTSP) can be used to model many practical problems. The mTSP is more complicated than the traveling salesman problem (TSP) because it requires determining which cities to assign to each salesman, as well as the optimal ordering of the cities within each salesman's tour. Previous studies proposed that Genetic Algorithm (GA), Integer Programming (IP) and several neural network (NN) approaches could be used to solve mTSP. This paper compared the results for mTSP, solved with Genetic Algorithm (GA) and Nearest Neighbor Algorithm (NNA). The number of cities is clustered into a few groups using k-means clustering technique. The number of groups depends on the number of salesman. Then, each group is solved with NNA and GA as an independent TSP. It is found that k-means clustering and NNA are superior to GA in terms of performance (evaluated by fitness function) and computing time.
Abstract: This paper proposes a novel solution for optimizing
the size and communication overhead of a distributed multiagent
system without compromising the performance. The proposed approach
addresses the challenges of scalability especially when the
multiagent system is large. A modified spectral clustering technique
is used to partition a large network into logically related clusters.
Agents are assigned to monitor dedicated clusters rather than monitor
each device or node. The proposed scalable multiagent system is
implemented using JADE (Java Agent Development Environment)
for a large power system. The performance of the proposed topologyindependent
decentralized multiagent system and the scalable multiagent
system is compared by comprehensively simulating different
fault scenarios. The time taken for reconfiguration, the overall computational
complexity, and the communication overhead incurred are
computed. The results of these simulations show that the proposed
scalable multiagent system uses fewer agents efficiently, makes faster
decisions to reconfigure when a fault occurs, and incurs significantly
less communication overhead.
Abstract: The present work demonstrates the design and simulation of a fuzzy control of an air conditioning system at different pressures. The first order Sugeno fuzzy inference system is utilized to model the system and create the controller. In addition, an estimation of the heat transfer rate and water mass flow rate injection into or withdraw from the air conditioning system is determined by the fuzzy IF-THEN rules. The approach starts by generating the input/output data. Then, the subtractive clustering algorithm along with least square estimation (LSE) generates the fuzzy rules that describe the relationship between input/output data. The fuzzy rules are tuned by Adaptive Neuro-Fuzzy Inference System (ANFIS). The results show that when the pressure increases the amount of water flow rate and heat transfer rate decrease within the lower ranges of inlet dry bulb temperatures. On the other hand, and as pressure increases the amount of water flow rate and heat transfer rate increases within the higher ranges of inlet dry bulb temperatures. The inflection in the pressure effect trend occurs at lower temperatures as the inlet air humidity increases.
Abstract: Data stream analysis is the process of computing
various summaries and derived values from large amounts of data
which are continuously generated at a rapid rate. The nature of a
stream does not allow a revisit on each data element. Furthermore,
data processing must be fast to produce timely analysis results. These
requirements impose constraints on the design of the algorithms to
balance correctness against timely responses. Several techniques
have been proposed over the past few years to address these
challenges. These techniques can be categorized as either dataoriented
or task-oriented. The data-oriented approach analyzes a
subset of data or a smaller transformed representation, whereas taskoriented
scheme solves the problem directly via approximation
techniques. We propose a hybrid approach to tackle the data stream
analysis problem. The data stream has been both statistically
transformed to a smaller size and computationally approximated its
characteristics. We adopt a Monte Carlo method in the approximation
step. The data reduction has been performed horizontally and
vertically through our EMR sampling method. The proposed method
is analyzed by a series of experiments. We apply our algorithm on
clustering and classification tasks to evaluate the utility of our
approach.
Abstract: Sorting appears the most attention among all computational tasks over the past years because sorted data is at the heart of many computations. Sorting is of additional importance to parallel computing because of its close relation to the task of routing data among processes, which is an essential part of many parallel algorithms. Many parallel sorting algorithms have been investigated for a variety of parallel computer architectures. In this paper, three parallel sorting algorithms have been implemented and compared in terms of their overall execution time. The algorithms implemented are the odd-even transposition sort, parallel merge sort and parallel rank sort. Cluster of Workstations or Windows Compute Cluster has been used to compare the algorithms implemented. The C# programming language is used to develop the sorting algorithms. The MPI (Message Passing Interface) library has been selected to establish the communication and synchronization between processors. The time complexity for each parallel sorting algorithm will also be mentioned and analyzed.
Abstract: The authors present a mixed method for reducing the order of the large-scale dynamic systems. In this method, the denominator polynomial of the reduced order model is obtained by using the modified pole clustering technique while the coefficients of the numerator are obtained by Pade approximations. This method is conceptually simple and always generates stable reduced models if the original high-order system is stable. The proposed method is illustrated with the help of the numerical examples taken from the literature.
Abstract: Ant colony based routing algorithms are known to
grantee the packet delivery, but they suffer from the huge overhead
of control messages which are needed to discover the route. In this
paper we utilize the network nodes positions to group the nodes
in connected clusters. We use clusters-heads only on forwarding
the route discovery control messages. Our simulations proved that
the new algorithm has decreased the overhead dramatically without
affecting the delivery rate.
Abstract: Social bookmarking is an environment in which
the user gradually changes interests over time so that the tag
data associated with the current temporal period is usually more
important than tag data temporally far from the current period.
This implies that in the social tagging system, the newly tagged
items by the user are more relevant than older items. This study
proposes a novel recommender system that considers the users-
recent tag preferences. The proposed system includes the
following stages: grouping similar users into clusters using an
E-M clustering algorithm, finding similar resources based on
the user-s bookmarks, and recommending the top-N items to
the target user. The study examines the system-s information
retrieval performance using a dataset from del.icio.us, which is
a famous social bookmarking web site. Experimental results
show that the proposed system is better and more effective than
traditional approaches.
Abstract: In the paper we submit the non-local modification of
kinetic Smoluchowski equation for binary aggregation applying to
dispersed media having memory. Our supposition consists in that that
intensity of evolution of clusters is supposed to be a function of the
product of concentrations of the lowest orders clusters at different
moments. The new form of kinetic equation for aggregation is
derived on the base of the transfer kernels approach. This approach
allows considering the influence of relaxation times hierarchy on
kinetics of aggregation process in media with memory.
Abstract: In molecular biology, microarray technology is widely and successfully utilized to efficiently measure gene activity. If working with less studied organisms, methods to design custom-made microarray probes are available. One design criterion is to select probes with minimal melting temperature variances thus ensuring similar hybridization properties. If the microarray application focuses on the investigation of metabolic pathways, it is not necessary to cover the whole genome. It is more efficient to cover each metabolic pathway with a limited number of genes. Firstly, an approach is presented which minimizes the overall melting temperature variance of selected probes for all genes of interest. Secondly, the approach is extended to include the additional constraints of covering all pathways with a limited number of genes while minimizing the overall variance. The new optimization problem is solved by a bottom-up programming approach which reduces the complexity to make it computationally feasible. The new method is exemplary applied for the selection of microarray probes in order to cover all fungal secondary metabolite gene clusters for Aspergillus terreus.
Abstract: The problem addressed herein is the efficient management of the Grid/Cluster intense computation involved, when the preconditioned Bi-CGSTAB Krylov method is employed for the iterative solution of the large and sparse linear system arising from the discretization of the Modified Helmholtz-Dirichlet problem by the Hermite Collocation method. Taking advantage of the Collocation ma-trix's red-black ordered structure we organize efficiently the whole computation and map it on a pipeline architecture with master-slave communication. Implementation, through MPI programming tools, is realized on a SUN V240 cluster, inter-connected through a 100Mbps and 1Gbps ethernet network,and its performance is presented by speedup measurements included.
Abstract: Self-organizing map (SOM) is a well known data
reduction technique used in data mining. It can reveal structure in
data sets through data visualization that is otherwise hard to detect
from raw data alone. However, interpretation through visual
inspection is prone to errors and can be very tedious. There are
several techniques for the automatic detection of clusters of code
vectors found by SOM, but they generally do not take into account
the distribution of code vectors; this may lead to unsatisfactory
clustering and poor definition of cluster boundaries, particularly
where the density of data points is low. In this paper, we propose the
use of an adaptive heuristic particle swarm optimization (PSO)
algorithm for finding cluster boundaries directly from the code
vectors obtained from SOM. The application of our method to
several standard data sets demonstrates its feasibility. PSO algorithm
utilizes a so-called U-matrix of SOM to determine cluster boundaries;
the results of this novel automatic method compare very favorably to
boundary detection through traditional algorithms namely k-means
and hierarchical based approach which are normally used to interpret
the output of SOM.