Abstract: Software Reusability is primary attribute of software
quality. There are metrics for identifying the quality of reusable
components but the function that makes use of these metrics to find
reusability of software components is still not clear. These metrics if
identified in the design phase or even in the coding phase can help us
to reduce the rework by improving quality of reuse of the component
and hence improve the productivity due to probabilistic increase in
the reuse level. In this paper, we have devised the framework of
metrics that uses McCabe-s Cyclometric Complexity Measure for
Complexity measurement, Regularity Metric, Halstead Software
Science Indicator for Volume indication, Reuse Frequency metric
and Coupling Metric values of the software component as input
attributes and calculated reusability of the software component. Here,
comparative analysis of the fuzzy, Neuro-fuzzy and Fuzzy-GA
approaches is performed to evaluate the reusability of software
components and Fuzzy-GA results outperform the other used
approaches. The developed reusability model has produced high
precision results as expected by the human experts.
Abstract: The most important subtype of non-Hodgkin-s
lymphoma is the Diffuse Large B-Cell Lymphoma. Approximately
40% of the patients suffering from it respond well to therapy,
whereas the remainder needs a more aggressive treatment, in order to
better their chances of survival. Data Mining techniques have helped
to identify the class of the lymphoma in an efficient manner. Despite
that, thousands of genes should be processed to obtain the results.
This paper presents a comparison of the use of various attribute
selection methods aiming to reduce the number of genes to be
searched, looking for a more effective procedure as a whole.
Abstract: With a rapid growth in 3D graphics technology over the last few years, people are desired to see more flexible reacting motions of a biped in animations. In particular, it is impossible to anticipate all reacting motions of a biped while facing a perturbation. In this paper, we propose a three-level tracking method for animating a 3D humanoid character. First, we take the laws of physics into account to attach physical attributes, such as mass, gravity, friction, collision, contact, and torque, to bones and joints of a character. The next step is to employ PD controller to follow a reference motion as closely as possible. Once the character cannot tolerate a strong perturbation to prevent itself from falling down, we are capable of tracking a desirable falling-down action to avoid any falling condition inaccuracy. From the experimental results, we demonstrate the effectiveness and flexibility of the proposed method in comparison with conventional data-driven approaches.
Abstract: Yogyakarta, as the capital city of Yogyakarta Province, has important roles in various sectors that require good provision of public transportation system. Ideally, a good transportation system should be able to accommodate the amount of travel demand. This research attempts to develop a trip generation model to predict the number of public transport passenger in Yogyakarta city. The model is built by using multiple linear regression analysis, which establishes relationship between trip number and socioeconomic attributes. The data consist of primary and secondary data. Primary data was collected by conducting household surveys which randomly selected. The resulted model is further applied to evaluate the existing TransJogja, a new Bus Rapid Transit system serves Yogyakarta and surrounding cities, shelters.
Abstract: A numerical study has been conducted to investigate the influence of fin pitch and relative humidity on the heat transfer performance of the fin-and-tube heat exchangers having plain fin geometry under dehumidifying conditions. The analysis is done using the ratio between the heat transfer coefficients in totally wet conditions and those in totally dry conditions using the appropriate correlations for both dry and wet conditions. For a constant relative humidity, it is found that the heat transfer coefficient increases with the increase of the air frontal velocity. By contrast, the fin efficiency decreases when the face velocity is increased. Apparently, this phenomenon is attributed to the path of condensate drainage. For the influence of relative humidity, the results showed an increase in heat transfer performance and a decrease in wet fin efficiency when relative humidity increases. This is due to the higher amount of mass transfer encountered at higher relative humidity. However, it is found that the effect of fin pitch on the heat transfer performance depends strongly on the face velocity. At lower frontal velocity the heat transfer increases with fin pitch. Conversely, an increase in fin pitch gives lower heat transfer coefficients when air velocity is increased.
Abstract: The radius-of-curvature (ROC) defines the degree of
curvature along the centerline of a roadway whereby a travelling
vehicle must follow. Roadway designs must encompass ROC in
mitigating the cost of earthwork associated with construction while
also allowing vehicles to travel at maximum allowable design speeds.
Thus, a road will tend to follow natural topography where possible,
but curvature must also be optimized to permit fast, but safe vehicle
speeds. The more severe the curvature of the road, the slower the
permissible vehicle speed. For route planning, whether for urban
settings, emergency operations, or even parcel delivery, ROC is a
necessary attribute of road arcs for computing travel time.
It is extremely rare for a geo-spatial database to contain ROC. This
paper will present a procedure and mathematical algorithm to
calculate and assign ROC to a segment pair and/or polyline.
Abstract: This paper presents an optimal and unsupervised satellite image segmentation approach based on Pearson system and k-Means Clustering Algorithm Initialization. Such method could be considered as original by the fact that it utilised K-Means clustering algorithm for an optimal initialisation of image class number on one hand and it exploited Pearson system for an optimal statistical distributions- affectation of each considered class on the other hand. Satellite image exploitation requires the use of different approaches, especially those founded on the unsupervised statistical segmentation principle. Such approaches necessitate definition of several parameters like image class number, class variables- estimation and generalised mixture distributions. Use of statistical images- attributes assured convincing and promoting results under the condition of having an optimal initialisation step with appropriated statistical distributions- affectation. Pearson system associated with a k-means clustering algorithm and Stochastic Expectation-Maximization 'SEM' algorithm could be adapted to such problem. For each image-s class, Pearson system attributes one distribution type according to different parameters and especially the Skewness 'β1' and the kurtosis 'β2'. The different adapted algorithms, K-Means clustering algorithm, SEM algorithm and Pearson system algorithm, are then applied to satellite image segmentation problem. Efficiency of those combined algorithms was firstly validated with the Mean Quadratic Error 'MQE' evaluation, and secondly with visual inspection along several comparisons of these unsupervised images- segmentation.
Abstract: Measuring the effect of perinatal lead exposure on learning ability of offspring is considered as a sensitive and selective index for providing an early marker for central nervous system damage produced by this toxic metal. A total of 35 Sprague-Dawley adult rats were used to investigate the effect of lead acetate toxicity on behavioral patterns of adult female rats and learning ability of offspring. Rats were allotted into 4 groups, group one received 1g/l lead acetate (n=10), group two received 1.5g/l lead acetate (n=10), group three received 2g/l lead acetate in drinking water (n=10) and control group did not receive lead acetate (n=5) from 8th day of pregnancy till weaning of pups.
The obtained results revealed a dose dependent increase in the feeding time, drinking frequency, licking frequency, scratching frequency, licking litters, nest building and retrieving frequencies, while standing time increased significantly in rats treated with 1.5g/l lead acetate than other treated groups and control, on contrary lying time decreased gradually in a dose dependent manner. Moreover, movement activities were higher in rats treated with 1g/l lead acetate than other treated groups and control. Furthermore, time spent in closed arms was significantly lower in rats given 2g/l lead acetate than other treated groups, while, they spent significantly much time spent in open arms than other treated groups which could be attributed to occurrence of adaptation. Furthermore, number of entries in open arms was dose dependent. However, the ratio between open/closed arms revealed a significant decrease in rats treated with 2g/l lead acetate than control group.
Abstract: In this paper we discuss the development of an Augmented Reality (AR) - based scientific visualization system prototype that supports identification, localisation, and 3D visualisation of oil leakages sensors datasets. Sensors generates significant amount of multivariate datasets during normal and leak situations. Therefore we have developed a data model to effectively manage such data and enhance the computational support needed for the effective data explorations. A challenge of this approach is to reduce the data inefficiency powered by the disparate, repeated, inconsistent and missing attributes of most available sensors datasets. To handle this challenge, this paper aim to develop an AR-based scientific visualization interface which automatically identifies, localise and visualizes all necessary data relevant to a particularly selected region of interest (ROI) along the virtual pipeline network. Necessary system architectural supports needed as well as the interface requirements for such visualizations are also discussed in this paper.
Abstract: The technique of k-anonymization has been proposed to obfuscate private data through associating it with at least k identities. This paper investigates the basic tabular structures that
underline the notion of k-anonymization using cell suppression.
These structures are studied under idealized conditions to identify the
essential features of the k-anonymization notion. We optimize data kanonymization
through requiring a minimum number of anonymized
values that are balanced over all columns and rows. We study the
relationship between the sizes of the anonymized tables, the value k, and the number of attributes. This study has a theoretical value through contributing to develop a mathematical foundation of the kanonymization
concept. Its practical significance is still to be
investigated.
Abstract: Video streaming over lossy IP networks is very
important issues, due to the heterogeneous structure of networks.
Infrastructure of the Internet exhibits variable bandwidths, delays,
congestions and time-varying packet losses. Because of variable
attributes of the Internet, video streaming applications should not
only have a good end-to-end transport performance but also have a
robust rate control, furthermore multipath rate allocation mechanism.
So for providing the video streaming service quality, some other
components such as Bandwidth Estimation and Adaptive Rate
Controller should be taken into consideration. This paper gives an
overview of video streaming concept and bandwidth estimation tools
and then introduces special architectures for bandwidth adaptive
video streaming. A bandwidth estimation algorithm – pathChirp,
Optimized Rate Controllers and Multipath Rate Allocation Algorithm
are considered as all-in-one solution for video streaming problem.
This solution is directed and optimized by a decision center which is
designed for obtaining the maximum quality at the receiving side.
Abstract: Accurate software cost estimates are critical to both
developers and customers. They can be used for generating request
for proposals, contract negotiations, scheduling, monitoring and
control. The exact relationship between the attributes of the effort
estimation is difficult to establish. A neural network is good at
discovering relationships and pattern in the data. So, in this paper a
comparative analysis among existing Halstead Model, Walston-Felix
Model, Bailey-Basili Model, Doty Model and Neural Network
Based Model is performed. Neural Network has outperformed the
other considered models. Hence, we proposed Neural Network
system as a soft computing approach to model the effort estimation
of the software systems.
Abstract: The objective of this study is to present the test
results of variable air volume (VAV) air conditioning system
optimized by two objective genetic algorithm (GA). The objective
functions are energy savings and thermal comfort. The optimal set
points for fuzzy logic controller (FLC) are the supply air temperature
(Ts), the supply duct static pressure (Ps), the chilled water
temperature (Tw), and zone temperature (Tz) that is taken as the
problem variables. Supply airflow rate and chilled water flow rate are
considered to be the constraints. The optimal set point values are
obtained from GA process and assigned into fuzzy logic controller
(FLC) in order to conserve energy and maintain thermal comfort in
real time VAV air conditioning system. A VAV air conditioning
system with FLC installed in a software laboratory has been taken for
the purpose of energy analysis. The total energy saving obtained in
VAV GA optimization system with FLC compared with constant air
volume (CAV) system is expected to achieve 31.5%. The optimal
duct static pressure obtained through Genetic fuzzy methodology
attributes to better air distribution by delivering the optimal quantity
of supply air to the conditioned space. This combination enhanced
the advantages of uniform air distribution, thermal comfort and
improved energy savings potential.
Abstract: The main objectives of this study were to identify
attributes that influence customer satisfaction and determine their
relationships with customer satisfaction. The variables included in
this research are place/ambience, food quality and service quality as
independent variables and customer satisfaction as the dependent
variable. A survey questionnaire which consisted of three parts to
measure demographic factors, independent variables, and dependent
variables was constructed based on items determined by past
research. 149 respondents from one of the well known hotel in Kuala
Lumpur, MALAYSIA were selected as a sample. Psychometric
testing was conducted to determine the reliability and validity of the
questionnaire. From the findings, there were positive significant
relationship between place/ambience (r=0.563**, p=0.000) and
service quality (r=0.544**, p=0.000) with customer satisfaction.
However, although relationship between food quality and customer
satisfaction was significant, it was in the negative direction (r=-
0.268**, p=0.001). New findings were discovered after conducting
this research and previous research findings were strengthened by the
results of this research. Future researchers could concentrate on
determining attributes that influence customer satisfaction when
cost/price is not a factor and reasons for place/ambience is currently
becoming the leading factor in determining customer satisfaction.
Abstract: The persistent nature of perfluorochemicals (PFCs) has attracted global concern in recent years. Perfluorooctanesulfonate (PFOS) and perfluorooctanoate (PFOA) are the most commonly found PFC compounds, and thus their fate and transport play key roles in PFC distribution in the natural environment. The kinetic behavior of PFOS or PFOA on boehmite consists of a fast adsorption process followed by a slow adsorption process which may be attributed to the slow transport of PFOS or PFOA into the boehmite pore surface. The adsorption isotherms estimated the maximum adsorption capacities of PFOS and PFOA on boehmite as 0.877 μg/m2 and 0.633 μg/m2, with the difference primarily due to their different functional groups. The increase of solution pH led to a moderate decrease of PFOS and PFOA adsorption, owing to the increase of ligand exchange reactions and the decrease of electrostatic interactions. The presence of NaCl in solution demonstrated negative effects for PFOS and PFOA adsorption on boehmite surfaces, with potential mechanisms being electrical double layer compression, competitive adsorption of chloride.
Abstract: Metrics is the process by which numbers or symbols
are assigned to attributes of entities in the real world in such a way as
to describe them according to clearly defined rules. Software metrics
are instruments or ways to measuring all the aspect of software
product. These metrics are used throughout a software project to
assist in estimation, quality control, productivity assessment, and
project control. Object oriented software metrics focus on
measurements that are applied to the class and other characteristics.
These measurements convey the software engineer to the behavior of
the software and how changes can be made that will reduce
complexity and improve the continuing capability of the software.
Object oriented software metric can be classified in two types static
and dynamic. Static metrics are concerned with all the aspects of
measuring by static analysis of software and dynamic metrics are
concerned with all the measuring aspect of the software at run time.
Major work done before, was focusing on static metric. Also some
work has been done in the field of dynamic nature of the software
measurements. But research in this area is demanding for more work.
In this paper we give a set of dynamic metrics specifically for
polymorphism in object oriented system.
Abstract: The paper describes a knowledge based system for
analysis of microscopic wear particles. Wear particles contained in
lubricating oil carry important information concerning machine
condition, in particular the state of wear. Experts (Tribologists) in the
field extract this information to monitor the operation of the machine
and ensure safety, efficiency, quality, productivity, and economy of
operation. This procedure is not always objective and it can also be
expensive. The aim is to classify these particles according to their
morphological attributes of size, shape, edge detail, thickness ratio,
color, and texture, and by using this classification thereby predict
wear failure modes in engines and other machinery. The attribute
knowledge links human expertise to the devised Knowledge Based
Wear Particle Analysis System (KBWPAS). The system provides an
automated and systematic approach to wear particle identification
which is linked directly to wear processes and modes that occur in
machinery. This brings consistency in wear judgment prediction
which leads to standardization and also less dependence on
Tribologists.
Abstract: In this paper we present a GP-based method for automatically evolve projections, so that data can be more easily classified in the projected spaces. At the same time, our approach can reduce dimensionality by constructing more relevant attributes. Fitness of each projection measures how easy is to classify the dataset after applying the projection. This is quickly computed by a Simple Linear Perceptron. We have tested our approach in three domains. The experiments show that it obtains good results, compared to other Machine Learning approaches, while reducing dimensionality in many cases.
Abstract: Automatic reusability appraisal is helpful in
evaluating the quality of developed or developing reusable software
components and in identification of reusable components from
existing legacy systems; that can save cost of developing the
software from scratch. But the issue of how to identify reusable
components from existing systems has remained relatively
unexplored. In this research work, structural attributes of software
components are explored using software metrics and quality of the
software is inferred by different Neural Network based approaches,
taking the metric values as input. The calculated reusability value
enables to identify a good quality code automatically. It is found that
the reusability value determined is close to the manual analysis used
to be performed by the programmers or repository managers. So, the
developed system can be used to enhance the productivity and
quality of software development.
Abstract: In this paper, a new learning algorithm based on a
hybrid metaheuristic integrating Differential Evolution (DE) and
Reduced Variable Neighborhood Search (RVNS) is introduced to train
the classification method PROAFTN. To apply PROAFTN, values of
several parameters need to be determined prior to classification. These
parameters include boundaries of intervals and relative weights for
each attribute. Based on these requirements, the hybrid approach,
named DEPRO-RVNS, is presented in this study. In some cases, the
major problem when applying DE to some classification problems
was the premature convergence of some individuals to local optima.
To eliminate this shortcoming and to improve the exploration and
exploitation capabilities of DE, such individuals were set to iteratively
re-explored using RVNS. Based on the generated results on
both training and testing data, it is shown that the performance of
PROAFTN is significantly improved. Furthermore, the experimental
study shows that DEPRO-RVNS outperforms well-known machine
learning classifiers in a variety of problems.